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Empirical Bayesian EM-based Motion Segmentation

Empirical Bayesian EM-based Motion Segmentation Nuno Vasconcelos Andrew Lippman

MIT Media Laboratory

20Ames St,E15-320M,Cambridge,MA02139

nuno,lip@https://www.wendangku.net/doc/0317349251.html,

Abstract

A recent trend in motion-based segmentation has been to rely on statistical procedures derived from expectation-maximization(EM)principles.EM-based approaches have various attractives for segmentation,such as proceeding by taking non-greedy soft decisions with regards to the assign-ment of pixels to regions,or allowing the use of sophisticated priors capable of imposing spatial coherence on the segmen-tation.A practical dif?culty with such priors is,however,the determination of appropriate values for their parameters.In this work,we exploit the fact that the EM framework is itself suited for empirical Bayesian data analysis to develop an algorithm that?nds the estimates of the prior parameters which best explain the observed data.Such an approach maintains the Bayesian appeal of incorporating prior be-liefs,but requires only a qualitative description of the prior, avoiding the requirement of a quantitative speci?cation of its parameters.This eliminates the need for trial-and-error strategies for the determination of these parameters and leads to better segmentations in less iterations.

1.Introduction

A digital world of ubiquitous computing,ultra-fast net-working,and on-line communities unveils a new set of re-quirements for digital video representations.New repre-sentations should be?exible enough to support interactiv-ity,provide suf?cient content clues for automated retrieval and classi?cation of content,and emphasize key features for scene understanding.Since these goals can be most naturally addressed in an object-domain(where scenes are characterized as compositions of object shapes,textures,and motion),the ability to decompose a video sequence into the set of objects that compose it and accurately describe their motion becomes important.

Image segmentation and motion(or optical?ow)estima-tion have been widely studied in the?elds of machine vision and image processing.Due to the dif?culty of the segmenta-tion problem,early approaches to optical?ow computation simply disregarded this component of the problem,relying on smoothness assumptions and regularization to overcome the ill-posed nature of optical?ow estimation[9,11].This, however,resulted in poor motion estimates and imposed strong constraints(such as the assumption of static scenes with camera motion,or simple scenes with a single moving object)on image analysis.It has been realized more recently that the problem can be solved only by procedures capable of jointly addressing the two components[12,5,14].This has led to a new generation of algorithms which iterate between optic?ow estimation and segmentation.

The idea is,for a given set of motion parameters and observed optic?ow,to?nd the maximum a posteriori prob-ability(MAP)estimate of the segmentation;and,given this segmentation,to?nd the set of motion parameters which maximizes the likelihood of the measured?ow.Because a hard-decision(regarding the membership of each pixel in the image to each of the segmentation classes)is performed for each iteration of these algorithms,they are sometimes referred to as clustering,or hard-decision algorithms.

From a statistical perspective,such algorithms can be seen as variations of a stochastic optimization proce-dure known as the Expectation-Maximization(EM)algo-rithm[6].EM-based motion segmentation treats the prob-lem as one of region-wise regression.The observed motion ?eld is seen as a realization of a stochastic process charac-terized by a Gaussian mixture density with as many compo-nents as the number of distinct regions in the video sequence. Segmentation masks(i.e.which region is responsible for each sample)are seen as hidden(non-observed)variables and the algorithm?nds the values of the motion parameters that maximize the likelihood of the observed data by iterat-ing between two steps.The E-step estimates the expected values of the hidden variables given the current values of the motion parameters and the observed data.The M-step then uses these expected values to?nd the set of parameters that maximize the likelihood of the data.

Because the region-assignment variables are binary,and expectations of binary values are equal to the probabilities

of the variables being“on”;the estimates computed in the E-step are nothing more than the posterior probability of the region-assignments given the observed optical?ow.I.e., EM is similar to the hard-decision algorithms above,but proceeds by taking soft-decisions,the MAP estimate of the segmentation being taken only upon the convergence of the iterative procedure.

Even though soft-decisions can lead to signi?cantly better performance than hard-decisions[17],there are additional attractives in using EM for segmentation.In particular, because it provides an elegant statistical framework for the segmentation problem,EM allows the use of sophisticated priors,such as Markov Random Fields(MRFs)to enforce spatial coherence on the segmentation[15,16].However, such priors are typically characterized by parameters whose values are dif?cult to determine a priori.In practice,these parameters are commonly set to arbitrary values,or adapted to the observed data through heuristic procedures.

In this work,we exploit the fact that the EM framework is itself suited for empirical Bayesian data analysis[3],and a well known approximation to the likelihood of MRF pro-cesses to develop an algorithm that?nds the estimates of the prior parameters which best explain the observed data. This eliminates the need for trial-and-error strategies for the determination of these parameters and leads to better segmentations in less EM iterations.

Section2provides a brief review of Bayesian data anal-ysis,and introduces the empirical Bayesian ideas that moti-vate our algorithm.The algorithm itself is then explained in detail in the remainder of the paper.Section3presents the doubly stochastic motion model on which all the statistical inferences are based.Section4discusses the parameter ini-tialization stage which provides a rough segmentation guess used to bootstrap the EM algorithm.The EM procedure for empirical Bayesian motion segmentation is presented in section5.Finally,section6illustrates the performance of the algorithm with some simulation examples and discusses the obtained results.

2.Bayesian and empirical Bayesian data anal-

ysis

In this section,we brie?y review Bayesian and empirical Bayesian procedures[13,3]for making inferences about the world,given observed image data.Assume that we are trying to make inferences about the world property?,given the image feature.Under the Bayesian framework,all inferences are based on the posteriori density function

???0

??00?0

(2)

instead of on equation1.

While from a perceptual standpoint such a hierarchical

structure has the appeal of modeling changes of prior be-

lief according to context(different contexts lead to different

values of0,altering the shape of the density which char-

acterizes prior beliefs),from a computational standpoint it

signi?cantly increases the complexity of the problem.After

all,the parameters of0are themselves random vari-

ables,as well as the parameters of their density functions,

and so on.We are therefore caught on a endless chain of con-

ditional probabilities which is computationally intractable.

These issues are generally ignored in practice,where pri-

ors are typically chosen in order to minimize computational

complexity,or set to arbitrary values.The latter solution is

prevalent in the MRF literature,where parameters are com-

monly set in arbitrary fashion or adapted using heuristics.

The alternative suggested by the empirical Bayesian phi-

losophy is to replace0by an estimate?0obtained as the

value which maximizes the marginal distribution0

as a function of0.Inferences are then based on equation1

using this estimated value.

While,strictly speaking,this approach violates the fun-

damental Bayesian principle that priors should not be esti-

mated from data,in practice it leads to more sensible so-

lutions than setting priors arbitrarily,or using priors whose

main justi?cation comes from computational simplicity(the

so-called conjugate priors).More importantly,it provides a

way to break the in?nite chain of conditional probabilities

mentioned above,while still allowing for different priors

depending on context.Consider,for example,the task of,

given pictures of a tree,to determine the probability of the

world property“color”from the image feature“pixel

color”.The standard Bayesian solution would be to

perform inferences based on equation1or,in this case, where,which is determined by the camera optics and sensor noise,relates world and pixel colors,and

expresses prior beliefs in tree colors according to the param-

eters.The main limitation of such model is that it fails to capture many factors that have an in?uence on tree colors,

such as geography(leaf colors vary from region to region),

seasonality(leaves are green in the Spring and yellow in the Fall),etc.Even though a simple prior may be appropriate to

describe the colors of a given type of tree,at a given time of

the year,in a given geographical location,no prior will be able to describe the colors of all trees,at all locations,for the

entire year.Better models are obviously possible by taking the route of equation2,i.e.by considering hyperpriors for

all these factors,at the cost of enduring a signi?cant increase

in complexity.

The empirical Bayesian perspective is to avoid this in-

crease by keeping the simple model,but choosing

the parameters that best explain the data.In this way,even though not directly,the model can account for the variations

above,as the estimated will be different for pictures taken

in different seasons,locations,etc.Choosing the which maximizes will originate a prior which favors green

colors for pictures taken in the Spring,and yellow colors for pictures taken in the Fall.In a sense,the empirical Bayesian

approach allows the observer to concentrate on the speci?ca-

tion the qualitative shape of the prior,letting the quantitative computation of prior parameters be inferred from the data.

Computationally,the bulk of work associated with em-

pirical Bayesian procedures relies on the search for the prior parameters that maximize the marginal likelihood0. Because these parameters are related to the observed image

features by the hidden world properties,

0??0?

the problem?ts naturally into an EM framework.Thus,

in practice,empirical Bayesian estimates are commonly ob-tained through EM procedures,that iterate between the com-putation of the expected values for the world properties,and the maximization over prior parameters.Therefore,the empirical Bayesian perspective not only supports the recent trend towards the application of EM for motion(and texture) segmentation,but extends it by providing a meaningful way to tune the priors to the observed data.

3.Doubly stochastic motion model

Our approach to image segmentation is based on linear parametric motion models,according to which the motion of the pixels associated with a given object is related to their image coordinates by

(3) where is the vector of pixel coordinates in the image plane,the pixel’s motion, and1the parameter vector which char-acterizes the motion of the entire object.In this work,we consider the particular case of af?ne motion where6,

1000

0001

(4)

and equation3models each of the components of the motion vector?eld as a plane in velocity space.

To account for uncertainties due to the imaging process, this motion model is embedded in a probabilistic framework, where pixels are associated with classes that have a one-to-one relationship with the objects in the scene.We assume that,conditional on the knowledge of image1and the class of pixel in image,the observed value of this pixel is the outcome of an independent identically distributed Gaussian random process characterized by

1(5) 1

exp

exp(6)

where is the random?eld of indicator vectors,

is the second order neighborhood of pixel(composed by the eight adjacent pixels),is the number of neighbors of pixel that belong to class,and is a normalizing constant or partition function.

This leads to a doubly stochastic motion model.Doubly stochastic random?elds using MRFs are the2-D extension of Hidden Markov Models(HMMs),and have long been used for texture modeling and segmentation[7,4,2].In particular,the prior of equation6has been shown to be a good model for segmentation masks(see for example?gure 5of[7])and extensively used in the texture analysis liter-ature.It is parameterized by the scalar and the vector

.controls the degree of clustering, 12

i.e.the likelihood of more or less class transitions between neighboring pixels,while the’s control the relative likeli-hood of each of the segmentation classes.

4.Parameter Initialization

For a typical video sequence,the likelihood of the ob-served image data is a complicated function of the segmen-tation and motion parameters.This presents a signi?cant challenge to EM-based algorithms since,given a poor initial estimate,EM will get trapped in undesirable local minima. The common approach to the problem of generating an ini-tial estimate is to generate a large number of possible mod-els and use clustering techniques to reduce the cardinality of this set to the number of regions(classes)into which the sequence is to be segmented[14].1

To initialize the EM algorithm,we start by computing the optic?ow between successive images using any of the conventional optical estimation techniques[1].We then split each image into rectangular tiles and,for each tile,?nd the set of parameters1which achieves the least squares?t between the motion model of equation3 and the measured optic?ow,according to the equations derived in appendix A2.Even though the population of motion models obtained through this?xed segmentation contains models which are close to the true models,it also contains a signi?cant number of outliers because many regions contain occlusion boundaries.These outliers are, typically,characterized by motion models associated with af?ne planes of large slope and intercept,which can ruin the performance of traditional clustering techniques,such as k-means.This is illustrated by?gure1.

This?gure illustrates a simple1D example consisting of an occlusion boundary between two translating objects.In a),the solid line represents the true optical?ow originated by two translating objects,and the dashed lines the best af?ne ?ts for each of three image tiles.As can be seen in b),if standard clustering is used to?nd the two parameter vectors associated with objects,the outlying model2will pull one of the estimates away from its correct value.Even a robust clustering technique will fail in this example,as

of region ,and we do not merge the regions.If not,we reverse the roles of and and repeat the test.If the null

hypothesis is rejected for any of the

1components of the motion parameter vector,the regions are not merged.I.e.,regions are only merged when there is strong evidence that they are not distinct.Region merging is performed by assigning all the initial square tiles associated with the pair of models under analysis to the model that best explains their motion in the mean square sense.

4.2.Outlier elimination

A conservative region-mergingstrategy such as the above is required to avoid improper merges,where a good model is combined with a model corrupted by outliers.The other step of our iterative procedure aims to detect these outlier models and eliminate them.For this,we rely on the following cross-validation procedure.

First,we start by considering a grid similar to the tile-grid used for the initial segmentation into ?xed size regions but displaced by half of the tile dimensions.Each of the

tiles

in this displaced grid is then warped according to all the current motion model candidates.The model that provides the best ?t between the warped tile and the the next image is then marked as a valid model.Finally,models that are not marked as valid for any of the tiles are elimi-nated from the list of candidates.The reasoning behind this cross-validation procedure is illustrated by ?gure 2,which is a replica of ?gure 1with the tile grid displaced.Because the grid is displaced,model 2no longer provides the best approximation to the optical ?ow of the second tile,which is better explained by model 1.As a result,2is identi?ed as an outlier,and deleted from the list of candidate mod-els,allowing any clustering technique to ?nd the correct solution.

R Figure 2.Elimination of outliers by cross-validation.

5.EM-based parameter estimation

Because they are computed over sets of tiles of arbitrary shape and granularity,the initial estimates are only a rough

approximation to the true motion parameters of the vari-ous image regions.The second stage of our algorithm uses the EM-based empirical Bayesian learning approach of sec-tion 2and the doubly stochastic motion model of section 3to:1)re?ne these initial estimates,2)?nd the MRF prior parameters which best explain the observed motion,and 3)compute the MAP class assignment for each image pixel.As mentioned in section 2,the fundamental computa-tional problem posed by the empirical Bayesian framework is that of maximizing the marginal likelihood of the observed data as a function of the motion and MRF parameters

where the summation is over all possible con?gurations of the hidden assignment variables vector ,is the vector of all motion and MRF parameters,and and 1are the

observed images.The pair

is usually referred to as the complete data and has log-likelihood

log

where is the component of the vector ,and where we have used the class conditional probabilities of equation 5,the conditional independence of the observa-tions given the indicator variables,and the binary nature of

.The EM algorithm maximizes the likelihood of the incomplete,observed,data by iterating between two steps which act on the log-likelihood of the complete data.

5.1.The E-step

The E-step computes the so-called

function de?ned by

log

(9)

where

are the parameters obtained in the previous it-eration and,for simplicity,we have dropped the depen-dence on 1.Under the MRF assumption for the prior class probabilities,the computation of and log becomes analytically intractable,and can only be addressed through Monte Carlo procedures such as Gibbs sampling [8].Such procedures are,however,ex-pensive from a computational perspective,and nesting a Gibbs sampler inside the EM iteration would lead to a pro-hibitive amount of computation.In order to simplify the problem,we rely on the well known approximation ?rst proposed by Besag in his iterated coding mode (ICM)pro-cedure for MAP estimation of MRF parameters [2],and later

used by Zhang et al.in the context of EM-based segmenta-tion[17].This approximation consists of replacing the true likelihood by the pseudo-likelihood

(10) and is an extension of the Markov properties of one dimen-sional chains,in which case the equation holds exactly.As-suming,further,that the con?guration of the MRF does not change drastically from one iteration of the EM algorithm to the next,the pseudo-likelihood can be approximated by

It is straightforward to show[17]that,under such approxi-mation,

1(11) from which

(12) where we also used the binary nature of the indicator vari-ables,and Bayes rule.Notice that the are the poste-rior class assignment probabilities given the observed im-ages.Given the current estimate of the prior probabilities

12,and the motion model pa-

rameters in,they are computed by substituting equa-tion5in equation11.

One possible problem with this computation is that a pixel whose motion is poorly explained by all the models

in will originate zero class-conditional likelihoods and the corresponding will be unde?ned.To avoid this problem,we rely on the fact that a pixel which cannot be

explained by any of the models is an outlier,and set the cor-responding to zero.Such a solution has the additional bene?t of producing robust estimates without increasing the complexity of the M-step.Once outliers are eliminated, equation10,and the computed’s are substituted in9,and the function becomes

log

log(13)5.2.The M-step

In the empirical Bayesian framework,the M-step maxi-mizes the function obtained in the E-step with respect to both the motion and MRF parameters.Substituting equa-tions5and6in equation13,we obtain

221

log

Since the?rst two terms on the right hand side of this equation do not depend on or and the third term does not depend on or,the maximization can be separated into two sub-problems.The?rst-maximization of with respect to the parameters of the class conditional pdf’s-is a variation of the non-linear least-squares problem found in optical?ow estimation,and is solvable by non-linear opti-mization techniques.In our implementation,we use a sim-pli?ed version of Newton’s method,where the terms which depend on second order image derivatives are disregarded, leading to the following iteration

121

(14)

where is the expected number of neighbors of pixel

that belong to the same class.Once the new values of the MRF parameters are computed,the prior probabilities 1are obtained by applying a single cycle of Besag’s ICM procedure:each pixel is visited in a raster scan order and,given the con?guration of its neighborhood,the corre-sponding are computed using equation6.It can be shown that is a concave function of and,guarantee-ing the existence of a single global maxima,and allowing fast convergence to the optimal value.

It is interesting to analyze the meaning of the equations above.The new motion parameters are what one would obtain by performing a weighted non-linear least squares-?t to the motion?eld that best aligns the two images.The parameter update does not,however,rely on a greedy bi-nary segmentation mask which is instead replaced by the posterior class assignment probabilities.I.e.the in?uence of a pixel on the least-squares?t to the motion parameters of a given class is proportional to the likelihood of the pixel belonging to that class.

The gradient update equations also have a nice intuitive meaning.A step in the direction of equation14changes the MRF parameter so that,at each pixel,the prior class-assignment probabilities move towards the posterior assign-ment probabilities obtained from the observed motion.Sim-ilarly,a step in the direction of equation15changes so that,at each pixel,the expected number of neighbors in the same state as the pixel is equal under both the prior and the posterior distributions.I.e.the EM algorithm sets the model parameters to the values that best explain the observed data, both in terms of class assignment probabilities and average number of neighbors in the same state as the neighborhood’s central pixel.

6.Experimental results and conclusions

In this section,we report on simulation results obtained with the“?ower garden”sequence.Figure3presents a frame from the sequence,and the estimate of the segmen-tation obtained by the parameter initialization algorithm of section4.While this segmentation mask is only a rough estimate of the true segmentation,it is able to capture the overall structure of the scene,discriminating the four regions that compose it.

Figure4illustrates the bene?ts of the empirical Bayesian solution to the motion segmentation problem that is now pro-posed.It presents three segmentations obtained after twenty iterations of the EM algorithm described in section5,the top two originated by setting the MRF parameters to arbitrary values,and the bottom one produced by the complete EM procedure(https://www.wendangku.net/doc/0317349251.html,ing equations14and15to compute these parameters).When the MRF parameters are set arbitrar-ily,the segmentation depends critically on the choice of the

50100150200250300350

100

150

200

Figure3.A frame from the input video sequence(left),and

the segmentation(right)originated by the parameter initialization

algorithm of section4.

clustering parameter.Small values of clustering,lead to noisy segmentations such as the one on the top of the?gure, while large values of originate segmentations with weakly de?ned region boundaries(notice the leakage between the house and sky regions and between the areas of tree detail and sky in the middle picture).

While it may be possible to obtain better results by a trial-and-error strategy for the determination of MRF pa-rameters,we were not able to obtain,in this way,a better segmentation than the originated by the empirical Bayesian approach,which is shown at the bottom of the?gure.The better performance of empirical Bayesian estimates can be understood by considering?gure5,which presents the evo-lution of the clustering parameter estimate as a function of the iteration number(for two different starting points).Once again,the result of empirical Bayesian parameter updating makes intuitive sense:while in early iterations(where un-certainty is high)clustering is small and pixels are free to wonder between regions,the clustering parameter increases as the EM procedure approaches convergence,and the seg-mentation“freezes”when this happens.

Even if such gradual evolution were not required for a good segmentation,it is not clear that the best trial-and-error estimate for a given sequence would be a good estimate for a different one.In fact,a review of the texture segmentation literature reveals a wide range of proposals for the value of 3,which did not include the values that worked best for us.The point is that using empirical Bayesian estimates eliminates the need for tedious trial-and-error procedures that are not always guaranteed to provide the best results.

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Figure4.Three motion based EM segmentations.For the top two,the MRF parameters were set to arbitrary values(top:02, middle:07).The bottom one was obtained with the empirical Bayesian parameter estimates discussed in the text.White pixels are outliers.

Figure5.Evolution of the clustering parameter as a function of iteration number.The two curves correspond to two different initial estimates of the parameter value.Notice that the evolution of is very insensitive to the initial estimate.

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A.Least squares optical?ow?t

It is well known that:1)modeling the optical?ow v(x) as

(16)

where is a set of iid zero-mean Gaussian random vari-ables and de?ned by the motion model of equation3, leads to a Gaussian likelihood function for the observed?ow; and2)the maximization of this likelihood with respect to the motion parameters is equivalent to the minimization of the mean squared error between the observed?ow and its prediction according to the model

1(17) where is the covariance matrix associated with. Substituting equation3into equation17,computing the gradient of E with respect to,and setting it to zero,we ?nd that the estimate of which provides the least squares ?t4is

(18)

Combining this equation with equation16,it can be easily shown that is a Gaussian random vector with mean and covariance

(19)

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第十八章无线电设备检测实验室设备配置建议一、概述：无线电设备检测工作是无线电管理的一个重要方面，随着科学技术水平的飞速发展，无线电通信应用愈来愈广泛，各种无线电通信设备迅速增加，新技术、新体制、新设备不断涌现，越来越多的无线电通信技术在我国得以应用。为了使有限的频谱资源能够科学地、有效地开发和利用，防止无线电设备本身产品质量不合格产生的各种有害干扰，从源头上减少无线电干扰信号，维护空中电波秩序，确保各种无线电设备正常进行，必须加强对各类无线电设备的管理，应积极开展无线电设备检测工作。现阶段在我国应用的通信技术体制非常广泛，目前 TDMA(GSM900/DCS1800/GPRS)、CDMA(cdmaOne)、PHS(小灵通)为代表的通信体制正在被中国移动、中国联通、中国电信、中国网通广泛的应用。同时，第三代（3G）移动通信系统(cdma2000、W-CDMA、TD-S-CDMA、LAS-CDMA 等)也正在紧锣密鼓的研究开发之中，在不远的将来可能得到应用。在当前的数字时代，除电信运营企业全部采用数字调制技术实现运营网络的数据、语音、图象等的无线传输外，其它应用场合也越来越多地应用数字调制技术，如DBS直接广播卫星通信、数字集群通信、Bluetooh蓝牙数据传输、WirelessLAN无线区域通信等。同时，各种新的系统如LMDS，WLAN，BLUETOOTH等不断投入使用。必将对无线电设备检测工作提出了更高的要求。我们认为，作为无线电频谱的管理部门，监管的重点首先是无线发射机系统的射频指标，如:发射总功率、频率准确度、占用带宽、杂散发射

等。在此基础上，可适当进行其他指标的测试，辅助运营商进行系统检测。其主要测试项目应包括： 1.频率准确度 2.最大输出功率 3.发射机互调 4.杂散发射 5.占用带宽 OBW、邻道功率ACP 6.相位误差另外，针对GSM系统，可增加调制及开关的频谱，功率时间曲线的测试；针对CDMA系统和扩频系统，可增加矢量幅度误差（EVM），码域功率和幅度统计特性（CCDF）等测试项目。为了确保检测工作的准确性、权威性，必须建立一个合格的检测实验室。它必须具备有效的质量管理体系、完全满足被测设备技术指标的测量仪器、经过培训的专业技术人员。以下重点讨论检测实验室需配备的仪器。二、检测实验室仪器仪表的配置应原则： 1．仪器功能强：应提供符合国家标准规范所要求的主要项目和指标。 2．测量基准高：测量结果应具备权威性，以便对网络设备和用户终端进行认证、验收、日常抽查、事故判别和质量检测。 3．通用性能好：应尽量基于单台设备平台对各种体制网络设备（TDMA、CDMA、PHS、CDMA2000、W-CDMA等）进行测量。 4．方便携带性：可以方便地在野外现场搭建测量系统。运输、搬移方便，抗震动和适应恶劣环境的能力强。

材料制备科学与技术

第七章单晶材料的制备名词解释： 1.熔盐生长法（助熔剂法、高温溶液法、熔盐法）是在高温下从熔融盐溶剂中生长晶体的方法。填空题： 1.气相生长法可以分为三类：升华法、蒸汽输运法、气相反应法 2.气体输运过程因其内部压力不同而主要有三种可能的方式（输运取决于什么东西？压力的三个级别）：①当压力<102Pa时，输运速度主要决定于原子速度②在102--3*105Pa之间的压力范围内，分子运动主要由扩散确定③当压力>3*105Pa 时，热对流对确定气体运动及其重要。 3.溶液中生长晶体的具体方法主要有：降温法、流动法（温差法）、蒸发法、凝胶法 4.水热法生长单晶体的设备装置是：高压釜 5.晶体与残余物的溶液分离开的方法有：倒装法和坩埚倾斜法。 6.从熔体中生长单晶体的典型方法大致有以下几种：（大类别和小类别都要写） ①正常凝固法a、晶体提拉法b、坩埚下降法（垂直式、水平式）c、晶体泡生法d、弧熔法②逐区溶化法a、水平区熔法b、浮区法c、基座法d、焰熔法③掺钕钇铝石 )晶体的B-S法生长榴石（Nd：YAG)晶体的提拉生长④硒镓银（AgGaSe 2 7.提拉法生长单晶体的加热方式有： 8.坩埚下降法即（B—S方法）的分类：垂直式、水平式简答题：（具体点） 1.气相生长法的分类 ①升华法：是将固体在高温区升华，蒸汽在温度梯度的作用下向低温区输运结晶的一种生长晶体的方法。 ②蒸汽输运法：是在一定的环境（如真空）下，利用运载气体生长晶体的方法，通常用卤族元素来帮助源的挥发和原料的输运，可以促进晶体的生长。 ③气相反应法：即利用气体之间的直接混合反应生成晶体的方法。 2.在气相系统中，通过可逆反应生长时，输运可以分为哪三个阶段？ ①在原料固体上的复相反应。 ②气体中挥发物的输运。 ③在晶体形成处的复相逆向反应。 3.气体输运过程因其内部压力不同而主要有哪三种可能的方式？ ①当压力<102Pa时，输运速度主要决定于原子速度。 ②在102--3*105Pa之间的压力范围内，分子运动主要由扩散确定。 ③当压力>3*105Pa时，热对流对确定气体运动及其重要。 4.溶液中生长晶体的具体方法主要有哪几种？每种方法的基本原理是什么？ ①降温法基本原理是利用物质较大的正溶解度温度系数，在晶体生长的过程中逐渐降低温度，使析出溶质不断在晶体上生长。

PID控制的基本原理

S lim e (t ) = 1 +RK t →∞ PID 控制的基本原理 1．PID 控制概述当今的自动控制技术绝大部分是基于反馈概念的。反馈理论包括三个基本要素：测量、比较和执行。测量关心的是变量，并与期望值相比较，以此误差来纠正和控制系统的响应。反馈理论及其在自动控制中应用的关键是：做出正确测量与比较后，如何用于系统的纠正与调节。在过去的几十年里，PID 控制，也就是比例积分微分控制在工业控制中得到了广泛应用。在控制理论和技术飞速发展的今天，在工业过程控制中 95%以上的控制回路都具有 PID 结构，而且许多高级控制都是以 PID 控制为基础的。 PID 控制器由比例单元（P ）、积分单元（I ）和微分单元（D ）组成，它的基本原理比较简单，基本的 PID 控制规律可描述为： G (S ) = K P + K 1 + K D S (1-1) PID 控制用途广泛，使用灵活，已有系列化控制器产品，使用中只需设定三个参数（ K P ， K I 和 K D ）即可。在很多情况下，并不一定需要三个单元，可以取其中的一到两个单元，不过比例控制单元是必不可少的。 PID 控制具有以下优点：（1）原理简单，使用方便，PID 参数 K P 、K I 和 K D 可以根据过程动态特性变化，PID 参数就可以重新进行调整与设定。（2）适应性强，按 PID 控制规律进行工作的控制器早已商品化，即使目前最新式的过程控制计算机，其基本控制功能也仍然是 PID 控制。PID 应用范围广，虽然很多工业过程是非线性或时变的，但通过适当简化，也可以将其变成基本线性和动态特性不随时间变化的系统，就可以进行 PID 控制了。（3）鲁棒性强，即其控制品质对被控对象特性的变化不太敏感。但不可否认 PID 也有其固有的缺点。PID 在控制非线性、时变、偶合及参数和结构不缺点的复杂过程时，效果不是太好；最主要的是：如果 PID 控制器不能控制复杂过程，无论怎么调参数作用都不大。在科学技术尤其是计算机技术迅速发展的今天，虽然涌现出了许多新的控制方法，但 PID 仍因其自身的优点而得到了最广泛的应用，PID 控制规律仍是最普遍的控制规律。PID 控制器是最简单且许多时候最好的控制器。在过程控制中，PID 控制也是应用最广泛的，一个大型现代化控制系统的控制回路可能达二三百个甚至更多，其中绝大部分都采用 PID 控制。由此可见，在过程控制中，PID 控制的重要性是显然的，下面将结合实例讲述 PID 控制。 1.1.1 比例（P ）控制比例控制是一种最简单的控制方式，其控制器的输出与输入误差信号成比例关系。当仅有比例控制时系统输出存在稳定误差。比例控制器的传递函数为： G C (S ) = K P (1- 2) 式中， K P 称为比例系数或增益（视情况可设置为正或负），一些传统的控制器又常用比例带（Proportional Band ， PB ），来取代比例系数 K P ，比例带是比例系数的倒数，比例带也称为比例度。对于单位反馈系统，0 型系统响应实际阶跃信号 R 0 1(t)的稳态误差与其开环增益 K 近视成反比，即： t →∞ 对于单位反馈系统，I 型系统响应匀速信号 (1- 3) R 1 (t)的稳态误差与其开环增益 K v 近视成反比, 即: lim e (t ) = R 1 K V (1- 4)

微生物实验室常用仪器配置

微生物实验室常用仪器配置微生物学实验室是生物学领域的一个基本实验室，对于一个完备的微生物学实验室，我们需要配置哪些仪器呢?环凯为您的微生物学实验仪器配置提供如下参考。 1、超净工作台微生物的培养都是在特定培养基中进行无菌培养，那么无菌培养必然需要超净工作台提供一个无菌的工作环境。 2、培养箱培养箱有多种类型，它的作用在于为微生物的生长提供一个适宜的环境。生化培养箱只能控制温度，可作为一般细菌的平板培养;霉菌培养箱可以控制温度和湿度，可作为霉菌的培养;CO2培养箱适用于厌氧微生物的培养。 3、天平天平用于精确称量各类试剂。实验室常用的是电子天平，电子天平按照精度不同有不同的级别。 4、微生物均质器

用于从固体样品中提取细菌。用微生物均质器制备微生物检测样本具有样品无污染、无损伤、不升温、不需要灭菌处理，不需洗刷器皿等特点，是微生物实验中使用较为方便的仪器。 5、菌落计数器菌落计数仪可协助操作者计数菌落数量。通过放大，拍照，计数等方式准确的获取菌落的数量。有些高性能的菌落计数器还可连接电脑完成自动计数的操作。 6、微波炉/电炉用于溶液的快速加热，微生物固体培养基的加热溶化。 7、高压灭菌锅微生物学所用到的大部分实验物品、试剂、培养基都应严格消毒灭菌。灭菌锅也有不同大小型号，有些是手动的，有些是全自动的。用户需要根据自己的需要选购。 8、移液器液体量器用于精密量取各类液体。常见的液体量器有量筒、移液管、微量取液器、刻度试管、烧杯。 9、低温冰箱冰箱是实验室保存试剂和样品必不可少的仪器。微生物学实验中用到的试剂有些要求是4度保存，有些要求是负20度保存，实验人员一定要看清试剂的保存条件，放置在恰当的温度下保存。 10、生物安全柜微生物实验中涉及的试剂和样品微生物有些是有毒的，对于操作人员来说伤害较大。为了防止有害悬浮微粒、气溶胶的扩散，可以利用生物安全柜对操作人员、样品及样品间交叉感染和环境提供安全保护。 11、摇床摇床是实验室常用的一种仪器，在微生物实验操作过程中，液体培养基培养细菌时需要在特定温度下振荡使用。 12、纯水装置

2012.3.18材料制备原理-课后作业题

第1章习题与思考题 1.1溶胶-凝胶合成 1、名词解释：（1）溶胶；（2）凝胶参考答案（列出了主要内容，根据具体情况自己总结，下同！）： 1、溶胶：是具有液体特征的胶体体系，是指微小的固体颗粒悬浮分散在液相中，不停地进行布朗运动的体系。分散粒子是固体或者大分子颗粒，分散粒子的尺寸在1~100nm之间，这些固体颗粒一般由103~109个原子组成。凝胶(Gel)：凝胶是具有固体特征的胶体体系，被分散的物质形成连续的网络骨架，骨架孔隙中充满液体或气体，凝胶中分散相含量很低，一般在1%~3%之间。 2、说明溶胶-凝胶法的原理及基本步骤。答：溶胶-凝胶法是一种新兴起的制备陶瓷、玻璃等无机材料的湿化学方法。其基本原理是：易于水解的金属化合物(无机盐或金属醇盐)在某种溶剂中与水发生反应，经过水解与缩聚过程逐渐凝胶化，再经干燥烧结等后处理得到所需材料，基本反应有水解反应和聚合反应。这种方法可在低温下制备纯度高、粒径分布均匀、化学活性高的单多组分混合物(分子级混合)，并可制备传统方法不能或难以制备的产物，特别适用于制备非晶态材料。溶胶-凝胶法制备过程中以金属有机化合物(主要是金属醇盐)和部分无机盐为前驱体，首先将前驱体溶于溶剂(水或有机溶剂)形成均匀的溶液，接着溶质在溶液中发生水解(或醇解)，水解产物缩合聚集成粒径为1nm左右的溶胶粒子(sol)，溶胶粒子进一步聚集生长形成凝胶(gel)。有人也将溶胶-凝胶法称为SSG法，即溶液-溶胶-凝胶法。 3、简述溶胶-凝胶制备陶瓷粉体材料的优点。答：①制备工艺简单、无需昂贵的设备； ②对多元组分体系，溶胶－凝胶法可大大增加其化学均匀性； ③反应过程易控制，可以调控凝胶的微观结构； ④材料可掺杂的范围较宽（包括掺杂量及种类），化学计量准确，易于改性； ⑤产物纯度高，烧结温度低 1.2水热与溶剂热合成 1、名词解释：（1）水热法；（2）溶剂热法。水热法：是指在特制的密闭反应器（高压釜）中，采用水溶液作为反应体系，通过对反应体系加热、加压(或自生蒸气压），创造一个相对高温、高压的反应环境，使得通常难溶或不溶的物质溶解，并且重结晶而进行无机合成与材料处理的一种有效方法。溶剂热法：将水热法中的水换成有机溶剂或非水溶媒（例如：有机胺、醇、氨、四氯化碳或苯等），采用类似于水热法的原理，以制备在水溶液中无法长成，易氧化、易水解或对水敏感的材料。 2、简述水热与溶剂热合成存在的问题？答：（1）水热条件下的晶体生长或材料合成需要能够在高压下容纳高腐蚀性溶剂的反应器，需要能被规范操作以及在极端温度压强条件下可靠的设备。由于反应条件的特殊性，致使水热反应相比较其他反应体系而言具有如下缺点： a 无法观察晶体生长和材料合成的过程，不直观。 b 设备要求高耐高温高压的钢材，耐腐蚀的内衬、技术难度大温压控制严格、成本高。 c 安全性差，加热时密闭反应釜中流体体积膨胀，能够产生极大的压强，存在极大的安全隐患。

PWM控制的基本原理

PWM控制的基本原理 PWM（Pulse Width Modulation）控制——脉冲宽度调制技术，通过对一系列脉冲的宽度进行调制，来等效地获得所需要波形（含形状和幅值）。 PWM控制技术在逆变电路中应用最广，应用的逆变电路绝大部分是PWM型，PWM 控制技术正是有赖于在逆变电路中的应用，才确定了它在电力电子技术中的重要地位。理论基础：冲量相等而形状不同的窄脉冲加在具有惯性的环节上时，其效果基本相同。冲量指窄脉冲的面积。效果基本相同，是指环节的输出响应波形基本相同。低频段非常接近，仅在高频段略有差异。图1形状不同而冲量相同的各种窄脉冲面积等效原理：分别将如图1所示的电压窄脉冲加在一阶惯性环节（R-L电路）上，如图2a所示。其输出电流i(t)对不同窄脉冲时的响应波形如图2b所示。从波形可以看出，在i(t)的上升段，i(t)的形状也略有不同，但其下降段则几乎完全相同。脉冲越窄，各i(t)响应波形的差异也越小。如果周期性地施加上述脉冲，则响应i(t)也是周期性的。用傅里叶级数分解后将可看出，各i(t)在低频段的特性将非常接近，仅在高频段有所不同。图2 冲量相同的各种窄脉冲的响应波形用一系列等幅不等宽的脉冲来代替一个正弦半波，正弦半波N等分，看成N个相连的脉冲序列，宽度相等，但幅值不等；用矩形脉冲代替，等幅，不等宽，中点重合，面积（冲量）相等，宽度按正弦规律变化。 SPWM波形——脉冲宽度按正弦规律变化而和正弦波等效的PWM波形。图3 用PWM波代替正弦半波要改变等效输出正弦波幅值，按同一比例改变各脉冲宽度即可。 PWM电流波：电流型逆变电路进行PWM控制，得到的就是PWM电流波。 PWM波形可等效的各种波形：直流斩波电路：等效直流波形 SPWM波：等效正弦波形，还可以等效成其他所需波形，如等效所需非正弦交流波形等，其基本原理和SPWM控制相同，也基于等效面积原理。随着电子技术的发展，出现了多种PWM技术，其中包括：相电压控制PWM、脉宽PWM 法、随机PWM、SPWM法、线电压控制PWM等，而本文介绍的是在镍氢电池智能充电器中采用的脉宽PWM法。它是把每一脉冲宽度均相等的脉冲列作为PWM波形，通过改变脉冲列的周期可以调频，改变脉冲的宽度或占空比可以调压，采用适当控制方法即可使电压与频率协调变化。可以通过调整PWM的周期、PWM的占空比而达到控制充电电流的目的。 PWM技术的具体应用

材料制备与合成

《材料制备与合成[料]》课程简介课程编号：02034916 课程名称：材料制备与合成/Preparation and Synthesis of Materials 学分: 2.5 学时：40 （课内实验(践)：0 上机：0 课外实践：0 ）适用专业：材料科学与工程建议修读学期：6 开课单位：材料科学与工程学院材料物理与化学系课程负责人：方道来先修课程：材料化学基础、物理化学、材料科学基础、金属材料学考核方式与成绩评定标准：期末开卷考试成绩（占80%）与平时考核成绩（占20%）相结合。教材与主要参考书目：教材：《材料合成与制备》. 乔英杰主编.国防工业出版社，2010年. 主要参考书目：1. 《新型功能材料制备工艺》, 李垚主编. 化学工业出版社，2011年. 2. 《新型功能复合材料制备新技术》.童忠良主编. 化学工业出版社，2010年. 3. 《无机合成与制备化学》. 徐如人编著. 高等教育出版社, 2009年. 4. 《材料合成与制备方法》. 曹茂盛主编. 哈尔滨工业大学出版社，2008年. 内容概述：本课程是材料科学与工程专业本科生最重要的专业选修课之一。其主要内容包括：溶胶-凝胶合成法、水热与溶剂热合成法、化学气相沉积法、定向凝固技术、低热固相合成法、热压烧结技术、自蔓延高温合成法和等离子体烧结技术等。其目的是使学生掌握材料制备与合成的基本原理与方法，熟悉材料制备的新技术、新工艺和新设备，理解材料的合成、结构与性能、材料应用之间的相互关系，为将来研发新材料以及材料制备新工艺奠定坚实的理论基础。 The course of preparation and synthesis of materials is one of the most important specialized elective courses for the undergraduate students majoring in materials science and engineering. It includes the following parts: sol-gel method, hydrothermal/solvothermal reaction method, CVD method, directional solidification technique, low-heating solid-state reaction method, hot-pressing sintering technique, self-propagating high-temperature synthesis, and SPS technique. Its purpose is to enable students to master the basic principles and methods of preparation and synthesis of materials, and grasp the new techniques, new processes and new equipments, and further understand the relationship among the synthesis, structure, properties and the applications of materials. The course can lay a firm theoretical foundation for the research and development of new materials and new processes in the future for students.

分子生物学实验室需要的仪器配置

分子生物学实验室需要的仪器配置 (1)培养箱在分子生物学试验中，有很多反应都是在特定温度下进行的，这时就需要一个控温的装置。例如：用于细菌的平板培养，我们通常设定为37℃于培养箱倒置培养；其他分子生物学实验如酶切等需要25℃，30℃，37℃等条件。 (2)冰箱冰箱是实验室保存试剂和样品必不可少的仪器。分子生物学实验中用到的试剂有些要求是4度保存，有些要求是负20度保存，实验人员一定要看清试剂的保存条件，放置在恰当的温度下保存。具体来说，不同温度下保存的物品如下： a. 4℃适合储存某些溶液、试剂、药品等。 b.-20℃适用于某些试剂、药品、酶、血清、配好的抗生素和DNA、蛋白质样品等。 c.-80℃适合某些长期低温保存的样品、大肠杆菌菌种、纯化的样品、特殊的低温处理消化液，感受态等的保存。 d.0-10℃的层析冷柜适合低温条件下的电泳、层析、透析等实验。 (3)摇床摇床是实验室常用仪器，一般有常温型和低温型两种。对于分子生物学实验室，如果能配置低温型摇床，就可以适应不同的实验需求。例如：用于大肠杆菌，酵母菌等生物工程菌种的振荡培养及蛋白的诱导表达，培养通常为28度和37度，诱导表达需要20-37度；在感受态的制备过程中，需要有18度的温度控制；用于蛋白凝胶的染色脱色时振荡，常温使用；用于大肠杆菌常规转化时振荡复苏，常为37度。对于控制温度低于室温时，我们需要低温型摇床来控温。 (4)水浴锅水浴锅也是一种控温装置，水浴控温对于样品来说比较快速且接触充分。例如，用于42度的大肠杆菌转化时的热激反应；用于DNA杂交过程中水浴控温。 (5)烘箱烘箱是用于灭菌和洗涤后的物品烘干。烘箱有不同的控温范围，用户可以根据实验需求进行选择。例如，有些塑料用具只能在42-45℃的烤箱中进行烘干；用于RNA方面的实验用具，需要在250℃烤箱中烘干。 (6)纯水装置纯水装置包括蒸馏水器和纯水机。蒸馏水器的价格便宜，但在造水过程中需要有人值守；纯水机价格高些，但是使用方便，可以储存一定量的纯水。纯水使用也有不同的级别，一般实验用水需要纯水，用于PCR、DNA测序、酶反应均需要超纯水。 (7)灭菌锅分子生物学所用到的大部分实验用具都应严格消毒灭菌。包括实验物品、试剂、培养基等。灭菌锅也有不同大小型号，有些是手动的，有些是全自动的。用户需要根据自己的需要选购。 (8)天平天平用于精确称量各类试剂。实验室常用的是电子天平，电子天平按照精度不同有不同的级别。

各行业实验室仪器基本配置

各行业实验室建设所需仪器基本配置各行业实验室仪器基本配置供水公司仪器名称具体应用 BOD测定仪测量水中生物需氧量双道原子荧光光度计可检测多种元素溶解氧测定仪测溶解氧数字浊度计测量液体浊度原子吸收分光光度计根据被测元素的基态原子对特征辐射的吸收程度进行定量分析实验室专用超纯水机清洗玻璃器皿、配置标准溶液、理化分析等，制备纯水和超纯水电导率仪测电解质溶液电导率值气相色谱仪定性、定量分析多功能红外测油仪测量水中油含量二氧化碳分析仪分析二氧化碳含量放射性测量仪测量水中放射性核素分光光度计定量分析傅里叶红外变换光谱仪定性分析光电式浑浊度仪测水的浑浊度总有机碳测定仪对水溶液中总有机碳进行定量测定离子色谱仪适用于亲水性阴、阳离子的分离酸度计测ＰＨ值显微镜观察微小物质火焰光度计测定元素含量激光粉尘仪测空气中粉尘含量露点仪测露点冷原子荧光测汞仪测汞含量微量氧分析仪测微量的氧气浓度油份浓度分析仪测定水中油的含量水气厂仪器名称具体应用离子色谱仪适用于亲水性阴、阳离子的分离液相色谱仪定性、定量分析实验室专用超纯水机清洗玻璃器皿、配置标准溶液、理化分析等，制备纯水和超纯水ＴＯＰＣ测定仪测量源水、饮水和超纯水的总有机碳含量火焰光度计分析二氧化碳含量浊度计测定元素含量露点仪测量液体浊度微量氧分析仪测露点原子吸收分光光度计测定水中微量氧

油分浓度分析仪根据被测元素的基态原子对特征辐射的吸收程度进行定量分析常量氧分析仪测定水中油的含量多功能红外测油仪测定水中氧傅里叶红外变换光谱仪定性分析溶解氧分析仪测溶解氧农产品质检站仪器名称具体应用酸度计测pＨ值电导率仪测电解质溶液电导率值液相色谱仪定性、定量分析实验室专用超纯水机清洗玻璃器皿、配置标准溶液、理化分析等，制备纯水和超纯水气相色谱仪定性、定量分析自动电位滴定仪酸碱滴定、氧化还原滴定、沉淀滴定、络合滴定紫外—可见分光光度计测量物质对不同波长单色辐射的吸收程度，定量分析可见分光光度计测量物质对不同波长单色辐射的吸收程度，定量分析原子吸收分光光度计根据被测元素的基态原子特征辐射的吸收程度进行定量分析红外分光光度计根据物质在红外光区的吸收光谱特征和朗伯比尔定律对物质进行定性定量分析卡尔费休水份仪测定含水的仪器傅立叶变换红外光谱仪定性、定量的分析色差计定性分析离子色谱仪检测农家产品中的微量水份微量水份测定仪用于易形成氢化物元素、易形成气态组分元素和易还原成原子蒸汽元素的测定荧光分光光度计测物质旋光度，分析物质的浓度、纯度、含糖量旋光仪（目视、自动）根据酶与底物能产生显色反应，对底物进行研究定性及定量分析酶标分析仪移取微量溶液微量移液器农产品中物质的定性定量分析气质联用仪测折射率阿贝尔折射仪测面粉、淀粉等粉剂的白度值白度计氨基酸含量氨基酸自动分析仪通过用标准色比较测颜色比较测色仪用未知浓度样品与已知浓度标物比较方法进行定量分析比色计测定粮食中含水量电脑粮食水分仪测蛋白质中氮的含量凯氏定氮仪测谷物中含水量谷物水份仪测黄曲霉素含量黄曲霉素测定仪测甲醛、氨含量甲醛氨测定仪测定空气中甲醛气体的含量甲醛测定仪测定谷物、面粉及其它含有淀粉的产品中淀粉酶活性降落值测定仪测混浊度浊度仪测含糖量、糖度

Empirical Bayesian EM-based Motion Segmentation

薄膜材料制备原理技术及应用

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