英文原文
An information-maximisation approach to
blind separation and blind deconvolution
Anthony J. Bell and Terrence J. Sejnowski
Computational Neurobiology Laboratory
The Salk Institute
100010 N.Torrey Pines Road
La Jolla, California 92037
Abstract
We derive a new self-organising learning algorithm which maximises the infor- mation transferred in a network of non-linear units. The algorithm does not assume any knowledge of the input distributions, and is defined here for the zero-noise limit. Under these conditions, information maximisation has extra properties not found in the linear case(Linsker 1989). The non-linearities in the transfer function are able to pick up higher-order moments of the input distributions and perform something akin to true redundancy reduction between units in the output representation. This enable- s the network to separate statistically independent components in the inputs: a higher -order generalisation of Principal Components Analysis.
We apply the network to the source separation (or cocktail party)problem, succe- ssfully separating unknown mixtures of up to ten speakers. We also show that a vari- ant on the network architecture is able to perform blind deconvolution (cancellation of unknown echoes and reverberation in a speech signal). Finally, we derive depend- encies of information transfer on time delays. We suggest that information maximi- sation provides a unifying framework for problems in …blind? signal processing.
1 Introduction
This paper presents a convergence of two lines of research. The first, the developme- nt of information theoretic unsupervised learning rules for neural networks has been pioneered by Linsker 1992, Becker & Hinton 1992, Atick & Redlich 1993, Plumble- y & Fallside 1988 and others. The second is the use,in signal processing, of higher-order statistics, for separating out mixtures of independent sources (blind sep- aration) or reversing the effect of an unknown filter (blind deconvolution). Methods exist for solving these problems, but it is fair to say that many of them are ad hoc. The literature displays a diversity of approaches and justifications—for historical re- views see (Comon 1994) and (Haykin 1994a).
In this paper, we supply a common theoretical framework for these problems through the use of information-theoretic objective functions applied to neural netwo- rks with non-linear units. The resulting learning rules have enabled a principled app- roach to the signal processing problems, and opened a new application area for info- rmation theoretic unsupervised learning.
Blind separation techniques can be used in any domain where an array of N r- eceivers picks up linear mixtures of N source signals. Examples include speech sep- aration (the …cocktail party problem?), processing of arrays of r adar or sonar signals, and processing of multi-sensor biomedical recordings. A previous approach has been implemented in analog VLSI circuitry for real-time source separation (Vittoz et al 1989, Cohen et al 1992). The application areas of blind deconvolution techniques in- clude the cancellation of acoustic reverberations (for example the …barrel effect? obs- erved when using speaker phones),the processing of geophysical data (seismic deco- nvolution) and the restorationof images.
The approach we take to th ese problems is a generalisation of Linsker?s infomax principle to non-linear units with arbitrarily distributed inputs uncorrupted by any k- nown noise sources. The principle is that described by
Laughlin (1981)(see Fig.1a): when inputs are to be passed through a sigmoid function, maximum information tra- nsmission can be achieved when the sloping part of the sigmoid is optimally lined up with the high density parts of the inputs. As we show, this can be achieved in an adaptive manner, using a stochastic gradient ascent rule. The generalisation of this r- ule to multiple units leads to a system which, in maximising information transfer, al- so reduces the redundancy between the units in the output layer. It is this latter proc- ess, also called
Independent Component Analysis (ICA), which enables the network to solve the blind separation task.
The paper is organised as follows. Section 2 describes the new information ma- ximisation learning algorithm, applied, respectively to a single input, an N N → mapping, a causal filter, a system with time delays and a …flexible? non-linearity. Section 3 describes the blind separation and blind deconvolution problems. Section 4 dis- cusses the conditions under which the information
maximisation process can find factorial codes (perform ICA),and therefore solve the separation and deconvo- lution problems. Section 5 presents results on the separation and deconvolution of speech signals. Section 6 attempts to place the theory and results in the context of previous work and mentions the limitations of the approach.
A brief report of this research appears in Bell & Sejnowski (1995)
2 Information maximisation
The basic problem tackled here is how to maximise the mutual information that the output Y of a neural network processor contains about its input X..
This is defined as:
(,)()()I Y X H Y H Y X =-∣ (1)
where ()H Y is the entropy of the output, while ()H Y X ∣ is whatever entropy the output has w hich didn?t come from the input. In the case that we have no noise (or r- ather, we don?t know what is noise and what is signal in the input),the mapping bet- ween X and Y is deterministic and ()H Y X ∣ has its
lowest possible value: it diverges to -∞. This divergence is one of the
consequences of the generalisation of infor- mation theory to continuous variables. What we call ()H Y is really the …differenti - al? entropy of Y with respect to some reference,such as the noise-level or the accura- cy of our discretisation of the variables in X and 1Y - .To avoid su- ch complexities, we consider here only the gradient of information theoretic quantities with respect to some parameter, w, in our network. Such gradients are as well-behaved as
discrete-variable entropies,because the reference terms involved in the definition of differential entropies disappear. The above equation can be differe- ntiated as follows, with respect to a parameter, w, involved in the mapping from X to Y :
(,)()I Y X H Y w w
??=?? (2) because ()H Y X ∣ does not depend on w . This can be seen by considering a system which avoids infinities: ()Y G X N =+, where G is some invertible transformatio- n and N is additive noise on the outputs. In this
case,()()H Y X H N ∣=( Nadal & Parga 1995). Whatever the level of this additive noise, maximisation of the mutual i- nformation, (,)I Y X , is equivalent to the maximisation of the output entropy, ()H Y , because ()()0w H N ??=.There is nothing mysterious about the determinis- tic case, despite the fact that ()H Y X ∣ tends to minus infinity as the noise variance goes to zero.
Thus for invertible continuous deterministic mappings, the mutual
information between inputs and outputs can be maximised by maximising the entropy of the out- puts alone.
2.1 For one input and one output
When we pass a single input x through a transforming function ()g x to give an output variable y , both (,)I y x and ()H Y are maximised when we align high density parts of the probability density function (pdf) of x with highly sloping parts of the function ()g x . This is the idea of “matching a
neuron?s input-output function to the expected distribution of signals” that we find in (Laughlin 1981) .
When ()g x is monotonically increasing or decreasing (ie: has a unique inver- se). the pdf of the output, ()y f y , can be written as a function of the pdf of the input, ()x f x .
()
()x y f x f y y x =∣??∣ (3)
where the bars denote absolute value. The entropy of the output, ()H Y ,is given by:
()[ln ()]()ln ()y y y H y E f y f y f y dy ∞
-∞=-=-? (4) where []E denotes expected value. Substituting (3) into (4) gives
[]()ln ln ()x y H y E E f x x ???=-?????
(5) The second term on the right (the entropy of x) may be considered to be unaffected by alterations in a parameter w determining g(x).Therefore in order to maximise the entropy of y by changing w , we need only concentrate on
maximising the first te- rm, which is the average log of how the input affects the output. This can be done by considering the …training set?of 'x s to approximate the density ()x f x , and derivin- g an …online? stochastic gradient ascent learning rule:
1
ln H y y y w w w x x w x -?????????????∝== ? ? ????????????? (6) In the case of the logistic transfer function:
11u y e
-=+, 0u wx w =+ (7) in which the input is multiplied by a weight w and added to a bias-weight
0w ,the terms above evaluate as:
(1)y wy y x
?=-? (8) (1)(1(12))y y y wx y w x ????=-+- ?????
(9) Dividing (9) by (8) gives the learning rule for the logistic function, as calculated fr- om the general rule of (6):
1(12)w x y w
?∝+- (10) Similar reasoning leads to the rule for the bias-weight:
012w y ?∝- (11)
The effect of these two rules can be seen in https://www.doczj.com/doc/bc4805258.html,. For example, if the input pdf ()x f x were gaussian, then the 0w ?-rule would centre the steepest part of the sigm- oid curve on the peak of ()x f x , matching input density to output slope, in a manner suggested intuitively by (3).The w ?-rule would then scale the slope of the sigmoid curve to match the variance of ()x f x . For example, narrow pdf?s would lead to sharply-sloping sigmoids. The w ?-rule is anti-Hebbian 2, with an anti-decay term. The anti-Hebbian term keeps y away from one uninformative situation: that of y being saturated at 0 or 1. But an anti-Hebbian rule alone makes weights go to zero, so the anti-decay term (1w ) keeps y away from the other uninformative situation: when w is so small that y stays around 0.5.
The effect of these two balanced forces is to produce an output pdf,
()y f y ,that is cl- ose to the flat unit distribution, which is the maximum entropy distribution for a var- iable bounded between 0 and 1.Fig.1b shows a family of these distributions, with the most informative one occurring at opt w .
A rule which maximises information for one input and one output may be suggestive for structures such as synapses and photoreceptors which must
position the gain of their non-linearity at a level appropriate to the average value and size of the input fl- uctuations (Laughlin 1981).However, to see the
advantages of this approach in artif- icial neural networks, we now analyse the case of multi-dimensional inputs and out- puts.
2.2 For an N N → network
Consider a network with an input vector x , a weight matrix W , a bias vector 0w and a monotonically transformed output vector 0()y g Wx w =+. Analogously to (3) ,the multivariate probability density function of y can be written:
()()x y f x f y J
= (12) where J is the absolute value of the Jacobian of the transformation. The Jacobian is the determinant of the matrix of partial derivatives:
1111det n n n n y y x x J y y x x ???? ??????= ???????? ??????
(13) The derivation proceeds as in the previous section except instead of maximizing ln y x ??，now we maximise ln J 。This latter quantity represents
the log of the volume of space in y into which points in x are mapped 。
中文翻译
一种解决盲分离和盲信号去卷积的信息最大化方
法
安东尼 J. 贝尔和 Terrence J. Sejnowski
计算的神经生物学实验室
索尔客学院
我们获得了新的自我学习算法-尽量增大在非线的单位网络中信息转化。该算法不使用任何输入分配信号，同时定义是在零噪音条件限制下。在这些情况之下，数据最大值化有在线性的情形下没有发现的其他的性质(Linsker 1989)。在转变过程中的非线性，能够在输出表现上获得高次序瞬间的输入分配和执行一些对真实的多余减少的同类型方法。这使网络能够在输入中以统计方法来分开独立的成份: 主要成份分析的高低次序一般化。
我们应用网络来处理源分离(或酒会)问题，成功地分开了多达十个人的话语。我们也说明在网络体系中的变量是能够做盲解卷的(对一个声音信号不知道卷积方式的反过程)。最后，我们得到信息转化在时延上的属性。我们表明数据最大值化提供了一个‘盲'信号处理问题的统一的架构。
1 介绍
这篇论文介绍了研究的二条路线的集中。第一个，神经网络在信息理论上的无人监督学习规则的发展已经被Linsker 1992、Becker & Hinton 1992 、Atick & Redlich 1993、Plumbley & Fallside 1988以及另外一些人所提出。其次是在信号的处理方面,使用高次序统计学，为了分开独立信源的混合体(盲分离)或者为了反向转化未知过滤后的结果(盲解卷)。存在解决这些问题的方法，但是公平的说他们中的大多数都是特例。文献显示了对历史的评论调查(Comon 1994)和(Haykin 1994a)的方法和辩论的不同。
在这篇论文里，通过使用被应用于带有非线性单位的神经网络的信息理论目标函数，我们为这些问题提供了一个公有理论框架。学习规则已经产生出一个逼近算法来解决信号处理问题，而且为信息理论上的无人监督方法打开一个新的应用领域。
盲分离技术可以应用于用一组N个接收器获得N个信号源线性混合的任何领域。例子包括讲话分离('鸡尾酒会问题')，一组雷达信号或声纳信号的处理, 和多感应生物医学的录音处理。早期的方法已经可以用类似VLSI电路来实现实时信源分离.(Vittoz等1989，科恩等1992)盲解卷技术的应用区域包括声音反响的消除，(例如，当使用话筒时观察到的听筒影响)，地球物理学数据的处理(地震波的解卷)和图象的修复。
我们解决这些问题的方法是Linsker的infomax原则通过任何已知的噪音来源对未分离的任意分配输入的非线性单位的一般化。Laughlin(1981)是这样描述这个原则的。当输入数据通过一个S字形函数时，在S字形的倾斜部份和输入数据高密度部分最佳地排成一行时信息最大化传输可以完成。正如我们所述,通过使用随机梯度上升规则，这可以用适应方式实现。这一规则对多个单位的一般化产生一个在数据转化最大化方面起到减少输出层单位之间的多余部分的系统。这个最新的算法，即独立的成份分析(ICA)，他能使网络能够解决盲分离工作。
论文是由以下各项组成。第2部分描述新的信息最大值化学习算法、应用、分别到单个输入、N N
映射、一个因果滤波器、一个有时间延迟的系统和一'灵活的'非线性。第3部分描述盲分离和盲解卷问题。第4部分讨论在什么情况下数据最大值化过程能找到阶乘编码(运行ICA)，因此来解决分离和解卷问题。第5部分介绍在分离和解卷语音信号方面的结果。第6部分尝试把理论和结果用于早期相关工作并且提到了方法的局限性。Bell 和Sejnowski在1995年发表了这个研究的简短报告。
2 信息最大值化
这里要处理的基本问题是该如何使互信息最大化，互信息即是类神经网路处理器的输出Y中包含输入X。
这被定义为:
(,)()()I Y X H Y H Y X =-∣ (1)
()H Y 是输出结果的熵，而()H Y X ∣是输出结果中没有来自输入信号部分的熵。在没有噪声(或者我们不知道在输入中什么是噪音什么是信息)的情况下，X 和Y 之间的映射是确定的以及()H Y X ∣到最低的可能数值:它到无穷小。这种分歧是对连续变量的信息论的一般化结果之一。我们所称的()H Y 只是Y 的‘微分'熵函数,Y 涉及到一些叁考值，像是噪声级别或我们在X 和Y 中的变量的离散化的准确性。为了避免这样的麻烦，在我们的网络中，我们只考虑有关于一些叁数的信息理论上的量的梯度—W 。这类梯度象离散变量熵函数一样正式，因为有关于微分熵的定义的叁考术语消失。上述等式可以依照下列各项来区别,使用有关变量—W ，W 与X 到Y 的映射有关。
(,)()I Y X H Y w w
??=?? (2) 因为()H Y X ∣并不依赖w 。这可以从考虑避免无限大的系统见出:()Y G X N =+,这里的G 是反向转化的函数和N 是输出信号的附加噪声。在这种情况下,()()H Y X H N ∣=( Nadal 和Parga 1995)。这种附加噪声的任何级别，互信息—(,)I Y X 的最大值与输出熵—()H Y 的最大值相等，因为()()0w H N ??=。尽管当噪声趋于零()H Y X ∣趋于负无穷大的事实，关于上面确定的事情也没有任何神秘的东西。如此对于连续确定性映射的逆转,在输入和输出之间的互信息值可以通过单个输出值的最大熵来最大化。
2.1 对于输入和输出
当我们把一个单独输入信号X 通过转化函数()g x ，得到输出变量y .当我们把x 的概率密度函数的高密度部分和函数()g x 非常倾斜部分结合起来时，(,)I y x 和()H Y 有最大值。这是把神经原的输入到输出的转化函数与信号的预期分配相结合的思想，我们可以在(Laughlin 1981)中找到。
当()g x 单调地增加或减少时，输出的信号的概率密度函数()y f y 可以当作输入信号概率密度函数()x f x 的函数；
()
()x y f x f y y x =∣??∣ (3)
这里竖条表示绝对值。输出值的熵函数()H Y 由下式表示:
()[ln ()]()ln ()y y y H y E f y f y f y dy ∞
-∞=-=-? (4) 这里[]E 指示数学期望。用公式(3) 代入公式(4)中得到
[]()ln ln ()x y H y E E f x x ???=-?????
(5) 等式右边的第二个式子(x 的熵函数)可以认为不会因为决定()g x 的叁数w 的改变而受到影响。因此为了通过改变w 来使y 的熵达到最大值，因此我们只需要使第一个式子值最大，这个式子反映输入影响输出的平均值。这可以通过认为x 的训练规则与密度()x f x 近似来完成，而且源自一随机倾度上升学问规则:
1
ln H y y y w w w x x w x -?????????????∝== ? ? ????????????? (6) 在实现逻辑转换功能时:
11u y e
-=+,0u wx w =+ (7) 在式子中输入x 与分量w 相乘，然后与次分量0w 相加上面的式子可等
价于:
(1)y wy y x
?=-? (8) (1)(1(12))y y y wx y w x ????=-+- ?????
(9) 当计算公式6的一般规则时，通过公式8（把学习规则和逻辑函数互换）来拆开公式9：
1(12)w x y w ?∝
+- (10) 类似方法得到次分量的式子: 012w y ?∝- (11)
例如，如果输入信号的概率密度函数()x f x 是高斯函数，然后0w ?式子会在()x f x 的最高处集中S 字形曲线的最陡峭部份，使输入信号概率密度与输出信号斜率相等，用由公式3直接推导出的方式。然后0w ?式子会度量S 字形曲线的倾斜度配合()x f x 的变化。例如，狭窄的概率密度函数会产生尖
锐倾斜的S 字形。0w ?规则是一个带无衰减anti-Hebbian 算法。Anti-Hebbian 算法使y 脱离一个不知道任何输入的环境：y 一定是0或1。但是当anti-Hebbian 算法将单位变成零，因此无衰减算法1w 使y 远离另一个不知道任何输入的环境:当w 足够小时，y 大约为0.5。
这两个均衡规则的作用是导出输出值的概率密度函数()x f x ，这是接近
最佳的单元分配，()x f x 是分布于0和1之间的变量的最大熵。图.1b 说明了
这些在opt w 处含最多信息的分量的整体。用于单输入单输出系统的信息最大化法则可能提示了一些象神经键和光感应器之类的结构，这类结构能够确定他们近似于输入波形平均大小和尺寸的曲线增益位置（Laughlin 1981）。然而正是看到这种算法在人工神经网络的优点，我们现在开始分析多变量的输入和输出。
2.2对于一个N N →网络
考虑一个输入矢量为x 、分量矩阵为W 、偏量矢量为0w 和单调转化输
出矢量0()y g Wx w =+的网络，与公式3相似，y 的多变量概率密度函数可以写为：
()()x y f x f y J
= （12） 这里J 是转换矩阵的雅克比行列式的绝对值，雅克比行列式是由分量的导数组成的矩阵的行列式。
1111det n n n n y y x x J y y x x ???? ??????= ???????? ??????
(13) 由上式引出，我们现在使ln J 最大化，而不是使ln y x ??最大化。ln J 的大小代表着指向被映射x 的y 的空间量。通过使lnJ 最大化，我们试图扩展我们的x 均匀指向y 的训练规则。对于g 是逻辑函数的E 皱型单位，()y g u =，0u Wx w =+。
中英文资料对照外文翻译 一、英文原文 A NEW CONTENT BASED MEDIAN FILTER ABSTRACT In this paper the hardware implementation of a contentbased median filter suitabl e for real-time impulse noise suppression is presented. The function of the proposed ci rcuitry is adaptive; it detects the existence of impulse noise in an image neighborhood and applies the median filter operator only when necessary. In this way, the blurring o f the imagein process is avoided and the integrity of edge and detail information is pre served. The proposed digital hardware structure is capable of processing gray-scale im ages of 8-bit resolution and is fully pipelined, whereas parallel processing is used to m inimize computational time. The architecturepresented was implemented in FPGA an d it can be used in industrial imaging applications, where fast processing is of the utm ost importance. The typical system clock frequency is 55 MHz. 1. INTRODUCTION Two applications of great importance in the area of image processing are noise filtering and image enhancement [1].These tasks are an essential part of any image pro cessor,whether the final image is utilized for visual interpretation or for automatic an alysis. The aim of noise filtering is to eliminate noise and its effects on the original im age, while corrupting the image as little as possible. To this end, nonlinear techniques (like the median and, in general, order statistics filters) have been found to provide mo re satisfactory results in comparison to linear methods. Impulse noise exists in many p ractical applications and can be generated by various sources, including a number of man made phenomena, such as unprotected switches, industrial machines and car ign ition systems. Images are often corrupted by impulse noise due to a noisy sensor or ch annel transmission errors. The most common method used for impulse noise suppressi on n forgray-scale and color images is the median filter (MF) [2].The basic drawback o f the application of the MF is the blurringof the image in process. In the general case,t he filter is applied uniformly across an image, modifying pixels that arenot contamina ted by noise. In this way, the effective elimination of impulse noise is often at the exp ense of an overalldegradation of the image and blurred or distorted features[3].In this paper an intelligent hardware structure of a content based median filter (CBMF) suita ble for impulse noise suppression is presented. The function of the proposed circuit is to detect the existence of noise in the image window and apply the corresponding MF
forced concrete structure reinforced with an overviewRein Since the reform and opening up, with the national economy's rapid and sustained development of a reinforced concrete structure built, reinforced with the development of technology has been great. Therefore, to promote the use of advanced technology reinforced connecting to improve project quality and speed up the pace of construction, improve labor productivity, reduce costs, and is of great significance. Reinforced steel bars connecting technologies can be divided into two broad categories linking welding machinery and steel. There are six types of welding steel welding methods, and some apply to the prefabricated plant, and some apply to the construction site, some of both apply. There are three types of machinery commonly used reinforcement linking method primarily applicable to the construction site. Ways has its own characteristics and different application, and in the continuous development and improvement. In actual production, should be based on specific conditions of work, working environment and technical requirements, the choice of suitable methods to achieve the best overall efficiency. 1、steel mechanical link 1.1 radial squeeze link Will be a steel sleeve in two sets to the highly-reinforced Department with superhigh pressure hydraulic equipment (squeeze tongs) along steel sleeve radial squeeze steel casing, in squeezing out tongs squeeze pressure role of a steel sleeve plasticity deformation closely integrated with reinforced through reinforced steel sleeve and Wang Liang's Position will be two solid steel bars linked Characteristic: Connect intensity to be high, performance reliable, can bear high stress draw and pigeonhole the load and tired load repeatedly.
外文文献： Knowledge of the stepper motor What is a stepper motor： Stepper motor is a kind of electrical pulses into angular displacement of the implementing agency. Popular little lesson: When the driver receives a step pulse signal, it will drive a stepper motor to set the direction of rotation at a fixed angle (and the step angle). You can control the number of pulses to control the angular displacement, so as to achieve accurate positioning purposes; the same time you can control the pulse frequency to control the motor rotation speed and acceleration, to achieve speed control purposes. What kinds of stepper motor sub-： In three stepper motors: permanent magnet (PM), reactive (VR) and hybrid (HB) permanent magnet stepper usually two-phase, torque, and smaller, step angle of 7.5 degrees or the general 15 degrees; reaction step is generally three-phase, can achieve high torque output, step angle of 1.5 degrees is generally, but the noise and vibration are large. 80 countries in Europe and America have been eliminated; hybrid stepper is a mix of permanent magnet and reactive advantages. It consists of two phases and the five-phase: two-phase step angle of 1.8 degrees while the general five-phase step angle of 0.72 degrees generally. The most widely used Stepper Motor. What is to keep the torque (HOLDING TORQUE) How much precision stepper motor? Whether the cumulative： The general accuracy of the stepper motor step angle of 3-5%, and not cumulative.
附录2：外文翻译 Robust Analysis of Feature Spaces: Color Image Segmentation Abstract A general technique for the recovery of significant image features is presented. The technique is based on the mean shift algorithm, a simple nonparametric procedure for estimating density gradients. Drawbacks of the current methods (including robust clustering) are avoided. Feature space of any nature can be processed, and as an example, color image segmentation is discussed. The segmentation is completely autonomous, only its class is chosen by the user. Thus, the same program can produce a high quality edge image, or provide, by extracting all the significant colors, a preprocessor for content-based query systems. A 512 512 color image is analyzed in less than 10 seconds on a standard workstation. Gray level images are handled as color images having only the lightness coordinate. Keywords: robust pattern analysis, low-level vision, content-based indexing
课程设计任务书 学生：专业班级：通信1103 指导教师：许建霞工作单位：信息学院 题目: 语音信号的盲分离 初始条件：Matlab软件、PC机 要求完成的主要任务:（包括课程设计工作量及其技术要求，以及说明书撰写等具体要求）设计任务 根据盲信号分离原理，用matlab采集两路以上的语音信号，选择合适的混合矩阵生成若干混合信号。选取合适的盲信号分离算法（如独立成分分析ICA等）进行训练学习，求出分离矩阵和分离后的语音信号。 设计要求 (1) 用matlab做出采样之后语音信号的时域和频域波形图 (2) 选择合适的混合矩阵，得到混合信号，并做出其时域波形和频谱图 (3) 采用混合声音信号进行训练学习，求出分离矩阵，编写出相应的确matlab代码。 (4) 用求出的分离矩阵从混合信号中分离出原语音信号，并画出各分离信号的时域波形和频谱图。 (5) 对结果进行对比分析。 时间安排：
指导教师签名：2014年 6 月10 日 系主任（或责任教师）签名：2014 年 6 月10 日 摘要 盲信号处理（Blind Signal Processing,BSP）是指从观测到的混合信号中，在没有任何先验条件的情况下，恢复出未知的源信号过程。盲信号分离已成为信号处理学界和通信工程学界共同感兴趣的一个极富挑战性的研究热点问题，并获得了迅速的发展。 盲分离根据信号源的不同可以分为确定信号盲分离、语音信号盲分离和图像盲分离等，本设计主要讨论语音信号的盲分离。 语音信号的盲分离主要是利用盲源分离（Blind Signal Separation,BSS）技术对麦克风检测到的一段语音信号进行处理，本文重点研究了以语音信号为背景的盲处理方法，在语音和听觉信号处理领域中，如何从混有噪声的的混叠语音信号中分离出各个语音源信号，来模仿人类的语音分离能力，成为一个重要的研究问题。根据盲信号分离原理，本设计用matlab采集3路语音信号，选择合适的混合矩阵生成若干混合信号。 具体实现主要结合独立分量分析ICA技术，选取混合矩阵对3个语音信号进行混合，并从混合信号中分离出原语音信号，最后画出各分离信号的时域波形和频谱图和原来的信号进行比较。此外还运用PCA算法进行了混合语音信号的分离实现，最终对两种算法进行比较。 关键字：盲信号处理；语音信号；盲源分离BSS；独立分量分析ICA技术
本科毕业设计（外文翻译） 题目居住区交往空间规划与设计 院（系部）xxx学院 专业名称xx 年级班级xx 学生姓名xx 指导教师xx xx 年xx 月x 日 Planning and Design of Association Space of residential District
Xia dong liang 【Abstract】:The association space refers to the indoor and outdoor space for communication between residents.The article presents an overall discussion of the necessity，hierarchy and functionality of association space,with a wish to create positive and healthy association atmosphere and stimulate good communication among residents so that the residential area can become a homeland full of love and harmony． 【Keyword】:residential area；association space；necessity；hierarchy；Functionality 【Foreword】：As the housing system reform and the rapid development of real estate, urban residential areas large urban settlements have emerged on the layout of residential buildings, public buildings, public green space, life and living facilities such as roads, to provide urban residents live in the community and The establishment, is an integral part of the city. Exchanges between the living room area residents is to communicate and exchange of indoor and outdoor space. At this stage, people's living standards greatly improved the living environment of continuous improvement district. Developers should not only focus on residential construction and the reasonable comfort, paying greater attention to the construction of residential environment. However, the current environment in the construction of residential areas, they are often the natural ecology of greening the environment is much more to consider, and the promotion of exchanges between the residents of the space environment to consider less, environmental construction can not meet the occupants of the psychological characteristics and needs. From the basic physiological needs gradually to meet the psychological and cultural fields of promoting a higher level, the residential area is not only the function of living, but also people's thinking and feelings of the local exchange. Therefore, the strengthening of exchanges between the residential areas of space construction, increase residential neighbourhood affinity, should be developed in the planning and construction of residential areas should also consider the issue. How to conduct exchanges between the residential areas of space planning and design, improve people's quality of life, the author of his own real estate development
杭州电子科技大学信息工程学院毕业设计（论文）外文文献翻译 毕业设计（论文）题目用单片机实现的数字时钟电路设计文献综述题目单片机控制系统系电子工程 专业电子信息科学与技术 姓名郭筱楠 班级08091911 学号08919115 指导教师王维平
单片机控制系统 广义地说，微型计算机控制系统（单片机控制系统）是用于处理信息的，这种被用于处理的信息可以是电话交谈，也可以是仪器的读数或者是一个企业的帐户，但是各种情况下都涉及到相同的主要操作：信息的处理、信息的存储和信息的传递。在常规的电子设计中，这些操作都是以功能平台方式组合起来的，例如计数器，无论是电子计数器还是机械计数器，都要存储当前的数值，并且按要求将该数值增加1。一个系统例如采用计数器的电子钟之类的任一系统要使其存储和处理能力遍布整个系统，因为每个计数器都能存储和处理一些数字。 现如今，以微处理器为基础的系统从常规的处理方法中分离了出来，它将信息的处理，信息的存储和信息的传输三个功能分离形成不同的系统单元。这种主要将系统分成三个主要单元的分离方法是冯-诺依曼在20世纪40年代所设想出来的，并且是针对微计算机的设想。从此以后基本上所有制成的计算机都是用这种结构设计的，尽管他们包含着宽广的物理形式与物理结构，但从根本上来说他们均是具有相同基本设计的计算机。 在以微处理器为基础的系统中，处理是由以微处理器为基础的系统自身完成的。存储是利用存储器电路，而从系统中输入和输出的信息传输则是利用特定的输入/输出（I/O）电路。要在一个以微处理器为基础的时钟中找出执行具有计数功能的一个特殊的硬件组成部分是不可能的，因为时间存储在存储器中，而在固定的时间间隔下由微处理器控制增值。但是，规定系统运转过程的软件却规定了包含实现计数器计数功能的单元部分。由于系统几乎完全由软件所定义，所以对微处理器结构和其辅助电路这种看起来非常抽象的处理方法使其在应用时非常灵活。这种设计过程主要是软件工程，而且在生产软件时，就会遇到产生于常规工程中相似的构造和维护问题。 图1.1 微型计算机的三个组成部分 图1.1显示出了微型计算机中这三个单元在一个微处理器控制系统中是如何按照机器中的信息通信方式而联接起来的。该系统由微处理器控制，微处理器能够对其自身的存储器和输入/输出单元的信息传输进行管理。外部的连接部分与
The research of digital image processing technique 1 Introduction Interest in digital image processing methods stems from two principal application areas: improvement of pictorial information for human interpretation; and processing of image data for storage, transmission, and representation for autonomous machine perception. This chapter has several objectives: (1)to define the scope of the field that we call image processing; (2)to give a historical perspective of the origins of this field; (3)to give an idea of the state of the art in image processing by examining some of the principal area in which it is applied; (4)to discuss briefly the principal approaches used in digital image processing; (5)to give an overview of the components contained in a typical, general-purpose image processing system; and (6) to provide direction to the books and other literature where image processing work normally is reporter. 1.1What Is Digital Image Processing? An image may be defined as a two-dimensional function, f(x, y), where x and y are spatial (plane) coordinates, and the amplitude of f at any pair of coordinates (x, y) is called the intensity or gray level of the image at that point. When x, y, and digital image. The field of digital image processing refers to processing digital images by means of a digital computer. Note that a digital image is composed of a finite number of elements, each of which has a particular location and value. These elements are referred to as picture elements, image elements, pels, and pixels. Pixel is the term most widely used to denote the elements of a digital image. We consider these definitions in more formal terms in Chapter2. Vision is the most advanced of our senses, so it is not surprising that images play the single most important role in human perception. However, unlike human who are limited to the visual band of the electromagnetic (EM) spectrum, imaging machines cover almost the entire EM spectrum, ranging from gamma to radio waves. They can operate on images generated by sources that human are not accustomed to associating with image. These include ultrasound, electron microscopy, and computer-generated images. Thus, digital image processing encompasses a wide and varied field of application. There is no general agreement among authors regarding where image processing stops and other related areas, such as image analysis and computer vision, start. Sometimes a distinction is made by defining image processing as a discipline in which both the input and output of a process are images. We believe this to be a limiting and somewhat artificial boundary. For example, under this definition, even the trivial task of computing the average intensity of an image (which yields a single number) would not be considered an image processing operation. On the other hand, there are fields such as computer vision whose ultimate goal is to use computer to
一、研究背景及意义 语音信号的分离近年来成为信号处理领域的一个研究热点，它在电话会议、助听器及便携设备、机器的语音识别方面有很多的应用与影响。而语音信号常使用盲信号处理的方法分离。 盲信号处理(Blind Source Processing)作为一种新兴的信号处理方法，逐步发展并得到了越来越多的关注。盲信号处理与现代信号处理朝向非平稳、非高斯、非线性的发展方向相吻合，有利于复杂信号的分析以及处理，其研究对象主要为非高斯信号。它在传统信号处理方法的基础上结合了信息论、统计学和人工神经网络的相关思想。如图1所示，所谓的“盲分离”是指在没有关于源信号本身以及传输信道的知识，对数据及系统参数没有太多先验知识的假设的情况下，如何从混迭信号(观测信号)中分离出各源信号的过程。它能适用于更广泛的环境，为许多受限于传统信号处理方法的实际问题提供了崭新的思路。 图1 盲分离的概念 在科学研究和工程应用中，很多观测信号都可以假设成是不可见的源信号的混合，如通信信号、图像、生物医学信号、雷达信号等等。例如经典的“鸡尾酒会”问题，在一个充满宾客的宴会厅里，我们每个人都会听到来自不同地方的声音，如音乐，歌声及说话声等，正常的人类拥有在这种嘈杂环境下捕捉到所感兴趣的语音的能力。可以看到，盲信号处理同传统信号处理方法最大的不同就在于用它致力于用最少的信息得到理想的处理结果。
盲信号分离可以有不同的分类方法。 根据所处理信号的不同，可以分为声纳信号盲分离，雷达信号盲分离，通信信号盲分离，语音信号盲分离，脑电信号盲分离等。 根据盲处理领域的不同，可以分为时域盲分离和频域盲分离。 根据传输信道的情况，可以分为无噪声，有加性噪声，有乘性噪声等。 根据源信号在传输信道中被混合方式的不同，可以分为瞬时混合，卷积混合，非线性混合等。 根据源信号和观测信号数目的不同，可以分为正定盲分离，欠定盲分离，过定盲分离等。 本文研究的主要内容是正定不含噪的卷积混合语音信号的频域盲分离 方法。 总的来说，盲信号分离是一种仅利用观测到的混合信号来估计源信号的方法，它是以独立分量分析(Independent Component Analysis,ICA)为理论基础的。与传统信号处理方法如FIR 滤波，小波分析等不同的是，它不要求有关于源信号本身以及信号传输通道的知识。受益于这种“盲”的条件，盲信号分离对多个领域有很大的促进作用，特别是它在声纳、雷达、通信、语音、图像等方面的应用对军事，国防科技的发展起着非常重要的作用。近十多年来，各国学者在盲信号分离领域展开了深入的研究，有了一系列的成果。本课题就是在这样的背景下对语音信号进行盲分离的研究，以探索新的算法，新的应用。 二、研究的基本内容，拟解决的主要问题 1.研究的基本内容 本课题详细研究语音分离的基本理论，重点研究卷积混合频域解法模型框架下的语音信号分离算法。 基于时域实值瞬时混合模型的盲分离算法已经研究的比较充分，但是在语音信号在现实中往往是卷积混合，而且在频域分离方法中信号是复值的，本文将研究利用复值信号特征的瞬时混合盲分离算法，对不同的复数域盲分
毕业设计英文资料翻译 Tran slati on ofthe En glish Docume nts for Graduati on Desig n 课题名称_____________________________________ 院< 系）_____________________________________ 专业 _____________________________________ 姓名 _____________________________________ 学号 _____________________________________ 起讫日期 _____________________________________ 指导教师 _____________________________________ 2018 年2月25日 原文: Abstract：Gree n buildi ng refers to do its best to maximize con servati on of resources (en ergy, land, water, and wood> , protecti ng the environment and reduce polluti on in its life cycle. Provide people with healthy, appropriate and efficient use of space, and nature in harmony symbiosis buildings. Idescribed more details of green building design ' notion, green building ' design, as well as the sig ni fica nee of the con
外文文献一单片机简介 单片机是一种集成在电路芯片，是采用超大规模集成电路技术把具有数据处理能力的中央处理器CPU随机存储器RAM、只读存储器ROM、多种I/O口和中断系统、定时器/计时器等功能（可能还包括显示驱动电路、脉宽调制电路、模拟多路转换器、A/D转换器等电路）集成到一块硅片上构成的一个小而完善的计算机系统。单片机也被称为微控制器（Microcontroller），是因为它最早被用在工业控制领域。单片机由芯片内仅有CPU的专用处理器发展而来。最早的设计理念是通过将大量外围设备和CPU集成在一个芯片中，使计算机系统更小，更容易集成进复杂的而对体积要求严格的控制设备当中。INTEL的Z80是最早按照这种思想设计出的处理器，从此以后，单片机和专用处理器的发展便分道扬镳。 二、单片机的发展趋势 现在可以说单片机是百花齐放，百家争鸣的时期，世界上各大芯片制造公司都推出了自己的单片机，从8位、16位到32位，数不胜数，应有尽有，有与主流C51系列兼容的，也有不兼容的，但它们各具特色，互成互补，为单片机的应用提供广阔的天地。 纵观单片机的发展过程，可以预示单片机的发展趋势，大致有： 1.低功耗CMOS MCS-51系列的8031推出时的功耗达630mW，而现在的单片机普遍都在100mW左右，随着对单片机功耗要求越来越低，现在的各个单片机制造商基本都采用了CMOS(互补金属氧化物半导体工艺)。象80C51就采用了HMOS(即高密度金属氧化物半导体工艺)和CHMOS(互补高密度金属氧化物半导体工艺)。CMOS虽然功耗较低，但由于其物理特征决定其工作速度不够高，而CHMOS则具备了高速和低功耗的特点，这些特征，更适合于在要求低功耗象电池供电的应用场合。所以这种工艺将是今后一段时期单片机发展的主要途径。 2.微型单片化 现在常规的单片机普遍都是将中央处理器(CPU)、随机存取数据存储(RAM)、只读程序存储器(ROM)、并行和串行通信接口，中断系统、定时电路、时钟电路集成在一块单一的芯片上，增强型的单片机集成了如A/D转换器、PMW(脉宽调制电路)、WDT(看门狗)、有些单片机将LCD(液晶)驱动电路都集成在单一的芯片上，这样 单片机包含的单元电路就更多，功能就越强大。甚至单片机厂商还可以根据用户的要求量身定做，制造出具有． 自己特色的单片机芯片。此外，现在的产品普遍要求体积小、重量轻，这就要求单片机除了功能强和功耗低外，还要求其体积要小。现在的许多单片机都具有多种封装形式，其中SMD(表面封装)越来越受欢迎，使得由单片机构成的系统正朝微型化方向发展。 3.主流与多品种共存 现在虽然单片机的品种繁多，各具特色，但仍以80C51为核心的单片机占主流，兼容其结构和指令系统的有PHILIPS公司的产品，ATMEL公司的产品和中国台湾
外文原文 Study on Human Resource Allocation in Multi-Project Based on the Priority and the Cost of Projects Lin Jingjing , Zhou Guohua SchoolofEconomics and management, Southwest Jiao tong University ,610031 ,China Abstract----This paper put forward the a ffecting factors of project’s priority. which is introduced into a multi-objective optimization model for human resource allocation in multi-project environment . The objectives of the model were the minimum cost loss due to the delay of the time limit of the projects and the minimum delay of the project with the highest priority .Then a Genetic Algorithm to solve the model was introduced. Finally, a numerical example was used to testify the feasibility of the model and the algorithm. Index Terms—Genetic Algorithm, Human Resource Allocation, Multi-project’s project’s priority . 1.INTRODUCTION More and more enterprises are facing the challenge of multi-project management, which has been the focus among researches on project management. In multi-project environment ,the share are competition of resources such as capital , time and human resources often occur .Therefore , it’s critical to schedule projects in order to satisfy the different resource demands and to shorten the projects’ duration time with resources constrained ,as in [1].For many enterprises ,the human resources are the most precious asset .So enterprises should reasonably and effectively allocate each resource , especially the human resource ,in order to shorten the time and cost of projects and to increase the benefits .Some literatures have