第一篇：國(guó)際會(huì)議--英文

表示歡迎：Good morning, Ladies and Gentlemen, I’m privileged to welcome you all to “The current status and future development of cotton machinery” conference.自我介紹：Let me introduce myself I am Dr Lifrom Beijing, China, and I am going to be the chair for this morning’s session.介紹議題：

This conference will focus on the discussion of the various aspects of cotton machinery.It includes the application of cotton machinery and the current status.And we will also discuss the prospect of cotton machinery.介紹報(bào)告人：

Now it gives me great pleasure to introduce today’sparticipants.They are :Han Dan, The director general of the United Nations Industrial Development Organization(聯(lián)合國(guó)工業(yè)發(fā)展組織總干事)

Zhang Hui, president of China Power investment group company.Shang Fengjiao, professor of Physics ,head of American Nuclear Energy

Association.Now, we welcome the opening of Han Dan do for us（現(xiàn)在，我們歡迎AA為我們做致開幕詞）

AA上臺(tái)

Today our first speaker isBB Hui.Let’s welcome

BB上臺(tái)

Thanks professor BB very much for her splendid report.After pro BB’s speech , do you have any questions, hands up please!

提問者提問

Any additional questions ?

Thank you once again for your excellent explanation

Now, let’s welcome the speech by Professor CC

CC上臺(tái)

Thank Professor CC for his excellent remarks CC.After pre CC’s report is there any specific question you would like to address to professor CC?

提問者提問

Any additional questions ?

Well, I am sure we could discuss longer, but unfortunately time is up.Thank you

very much, Dr CC

Thanks for the excellent report of the two experts.Finally Let’s welcome to Professor AA, give us a summary of conference.AA上臺(tái)（報(bào)告總結(jié)，宣布閉幕）

I’d like to thank all the representatives for their excellent remarks.Also I should thank all the organizing committee for their arrangement and organization.Meanwhile, I hope you’ll enjoy your stay in Harbin.

第二篇：英文國(guó)際會(huì)議講稿

PPT(1)大家上午好！今天我匯報(bào)的主題是：基于改進(jìn)型LBP算法的運(yùn)動(dòng)目標(biāo)檢測(cè)系統(tǒng)。運(yùn)動(dòng)目標(biāo)檢測(cè)技術(shù)能降低視頻監(jiān)控的人力成本，提高監(jiān)控效率，同時(shí)也是運(yùn)動(dòng)目標(biāo)提取、跟蹤及識(shí)別算法的基礎(chǔ)。圖像信號(hào)具有數(shù)據(jù)量大，實(shí)時(shí)性要求高等特征。隨著算法的復(fù)雜度和圖像清晰度的提高，需要的處理速度也越來(lái)越高。幸運(yùn)的是，圖像處理的固有特性是并行的，尤其是低層和中間層算法。這一特性使這些算法，比較容易在FPGA等并行運(yùn)算器件上實(shí)現(xiàn)，今天匯報(bào)的主題就是關(guān)于改進(jìn)型LBP算法在硬件上的實(shí)現(xiàn)。

good morning everyone.My report is about a Motion Detection System Based on Improved LBP Operator.Automatic motion detection can reduce the human cost of video surveillance and improve efficiency [?'f??(?)ns?]，it is also the fundament of object extraction, tracking and recognition [rek?g'n??(?)n].In this work, efforts ['ef?ts] were made to establish the background model which is resistance to the variation of illumination.And our video surveillance system was realized on a FPGA based platform.PPT(2)

目前，常用的運(yùn)動(dòng)目標(biāo)檢測(cè)算法有背景差分法、幀間差分法等。幀間差分法的基本原理是將相鄰兩幀圖像的對(duì)應(yīng)像素點(diǎn)的灰度值進(jìn)行減法運(yùn)算，若得到的差值的絕對(duì)值大于閾值，則將該點(diǎn)判定為運(yùn)動(dòng)點(diǎn)。但是幀間差分檢測(cè)的結(jié)果往往是運(yùn)動(dòng)物體的輪廓，無(wú)法獲得目標(biāo)的完整形態(tài)。

Currently, Optic Flow, Background Subtraction and Inter-frame difference are regard as the three mainstream algorithms to detect moving object.Inter-frame difference based method need not model ['m?dl] the background.It detects moving objects based on the frame difference between two continuous frames.The method is easy to be implemented and can realize real-time detection, but it cannot extract the full shape of the moving objects [6].PPT(3)

在攝像頭固定的情況下，背景差分法較為簡(jiǎn)單，且易于實(shí)現(xiàn)。若背景已知，并能提供完整的特征數(shù)據(jù)，該方法能較準(zhǔn)確地檢測(cè)出運(yùn)動(dòng)目標(biāo)。但在實(shí)際的應(yīng)用中，準(zhǔn)確的背景模型很難建立。如果背景模型如果沒有很好地適應(yīng)場(chǎng)景的變化，將大大影響目標(biāo)檢測(cè)結(jié)果的準(zhǔn)確性。像這副圖中，背景模型沒有及時(shí)更新，導(dǎo)致了檢測(cè)的錯(cuò)誤。

The basic principle of background removal method is building a background model and providing a classification of the pixels into either foreground or background [3-5].In a complex and dynamic environment, it is difficult to build a robust [r?(?)'b?st] background model.PPT(4)

上述的幀間差分法和背景差分法都是基于灰度的?；诨叶鹊乃惴ㄔ诠庹諚l件改變的情況下，性能會(huì)大大地降低，甚至失去作用。

The algorithms we have discussed above are all based on grayscale.In practical applications especially outdoor environment, the grayscales of each pixel are unpredictably shifty because of the variations in the intensity and angle of illumination.PPT(5)為了解決光照改變帶來(lái)的基于灰度的算法失效的問題，我們考慮用紋理特征來(lái)檢測(cè)運(yùn)動(dòng)目標(biāo)。而LBP算法是目前最常用的表征紋理特征的算法之一。首先在圖像中提取相鄰9個(gè)像素點(diǎn)的灰度值。然后對(duì)9個(gè)像素中除中心像素以外的其他8個(gè)像素做二值化處理。大于等于中心點(diǎn)像素的，標(biāo)記為1，小于的則標(biāo)記為0。最后將中心像素點(diǎn)周圍的標(biāo)記值按統(tǒng)一的順序排列，得到LBP值，圖中計(jì)算出的LBP值為10001111。當(dāng)某區(qū)域內(nèi)所有像素的灰度都同時(shí)增大或減小一定的數(shù)值時(shí)，該區(qū)域內(nèi)的LBP值是不會(huì)改變的，這就是LBP對(duì)灰度的平移不變特性。它能夠很好地解決灰度受光照影響的問題。

In order to solve the above problems, we proposed an improved LBP algorithm which is resistance to the variations of illumination.Local binary pattern(LBP)is widely used in machine vision applications such as face detection, face recognition and moving object detection [9-11].LBP represents a relatively simple yet powerful texture descriptor which can describe the relationship of a pixel with its immediate neighborhood.The fundamental of LBP operator is showed in Fig 1.The basic version of LBP produces 256 texture patterns based on a 9 pixels neighborhood.The neighboring pixel is set to 1 or 0 according to the grayscale value of the pixel is larger than the value of centric pixel or not.For example, in Fig1 7 is larger than 6, so the pixel in first row first column is set to 1.Arranging the 8 binary numbers in certain order, we get an 8 bits binary number, which is the LBP pattern we need.For example in Fig.1, the LBP is 10001111.LBP is tolerant ['t?l(?)r(?)nt] against illumination changing.When the grayscales of pixels in a 9 pixels window are shifted due to illumination changing, the LBP value will keep unchanged.PPT(6)

圖中的一些常見的紋理，都能用一些簡(jiǎn)單的LBP向量表示，對(duì)于每個(gè)像素快，只需要用一個(gè)8比特的LBP值來(lái)表示。

There are some textures , and they can be represent by some simple 8bit LBP patterns.PPT(7)

從這幅圖也可以看出，雖然灰度發(fā)生了很大的變化，但是紋理特征并沒有改變，LBP值也沒有變化。

You can see, in these picture , although the grayscale change alot, but the LBP patterns keep it value.PPT(8)上述的算法是LBP算法的基本形式，但是這種基本算法不適合直接應(yīng)用在視頻監(jiān)控系統(tǒng)中。主要有兩個(gè)原因：第一，在常用的視頻監(jiān)控系統(tǒng)中，特別是在高清視頻監(jiān)控系統(tǒng)中，9個(gè)像素點(diǎn)覆蓋的區(qū)域很小，在如此小的區(qū)域內(nèi)，各個(gè)像素點(diǎn)的灰度值十分接近，甚至是相同的，紋理特征不明顯，無(wú)法在LBP值上體現(xiàn)。第二，由于以像素為單位計(jì)算LBP值，像素噪聲會(huì)造成LBP值的噪聲。這兩個(gè)原因?qū)е掠?jì)算出的LBP值存在較大的隨機(jī)性，甚至在靜止的圖像中，相鄰兩幀對(duì)應(yīng)位置的LBP值也可能存在差異，從而引起的誤檢測(cè)。

為了得到更好的檢測(cè)性能，我們采用基于塊均值的LBP算法。這種方法的基本原理是先計(jì)算出3×3個(gè)像素組成的的像素塊的灰度均值，以灰度均值作為該像素塊的灰度值。然后以3×3個(gè)像素塊（即9×9個(gè)像素）為單位，計(jì)算LBP值。

The typical LBP cannot meet the need of practical application of video surveillance for two reasons: Firstly, a “window” which only contains 9 pixels is a small area in which the grayscales of pixels are similar or same to each other, and the texture feature in such a small area is too weak to be reflected by a LBP.Secondly, pixel noise will immediately cause the noise of LBP, which may lead to a large number of wrong detection.In order to obtain a better performance, we proposed an improved LBP based on the mean value of “block”.In our algorithm, one block contains 9 pixels.Compared with original LBP pattern calculated in a local 9 neighborhood between pixels, the improved LBP operator is defined by comparing the mean grayscale value of central block with those of its neighborhood blocks(see Fig.2).By replacing the grayscales of pixels with the mean value of blocks, the effect of the pixel noise is reduced.The texture feature in such a bigger area is more significant to be described by LBP pattern.PPT(9)

運(yùn)用LBP描述背景，其本質(zhì)上也是背景差分法的一種。背景差分法應(yīng)用在復(fù)雜的視頻監(jiān)控場(chǎng)景中時(shí)，要解決建立健壯的背景模型的問題。駛?cè)氩⑼２丛诒O(jiān)控畫面中的汽車，被搬移出監(jiān)控畫面的箱子等，都會(huì)造成背景的改變。而正確的背景模型是正確檢測(cè)出運(yùn)動(dòng)目標(biāo)并提取完整目標(biāo)輪廓的基礎(chǔ)。如果系統(tǒng)能定時(shí)更新背景模型，將已經(jīng)移動(dòng)出監(jiān)控畫面的物體“剔除”出背景模型，將進(jìn)入監(jiān)控畫面并且穩(wěn)定停留在畫面中的物體“添加”入背景模型，會(huì)減少很多由于背景改變而造成的誤檢測(cè)。

根據(jù)前一節(jié)的介紹，幀間差分法雖然無(wú)法提取完整的運(yùn)動(dòng)目標(biāo)，但是它是一種不依賴背景模型就能進(jìn)行運(yùn)動(dòng)目標(biāo)檢測(cè)的算法。因此，可以利用幀間差分法作為當(dāng)前監(jiān)控畫面中是否有運(yùn)動(dòng)目標(biāo)的依據(jù)。如果畫面中沒有運(yùn)動(dòng)目標(biāo)，就定期對(duì)背景模型進(jìn)行更新。如果畫面中有運(yùn)動(dòng)目標(biāo)，就推遲更新背景模型。這樣就能避免把運(yùn)動(dòng)目標(biāo)錯(cuò)誤地“添加”到背景模型中。

In practical application, the background is changing randomly.For traditional background subtraction algorithm the incapability of updating background timely will cause wrong detection.In order to solve this problem, we propose an algorithm with dynamic self updating background model.As we know, Inter-frame difference method can detect moving object without a background model, but this method cannot extract the full shape.Background subtraction method can extract the full shape but needs a background model.The basic principle of our algorithm is running a frame difference moving object detection process concurrently [k?n'k?r?ntli] with the background subtraction process.What’s time to update the background is according to the result of frame difference detection.PPT(10)

運(yùn)動(dòng)目標(biāo)檢測(cè)系統(tǒng)特別是嵌入式運(yùn)動(dòng)目標(biāo)檢測(cè)系統(tǒng)在實(shí)際應(yīng)用中要解決實(shí)時(shí)性的問題。比如每秒60幀的1024×768的圖像，對(duì)每個(gè)像素都運(yùn)用求均值，求LBP等算法，那么它的運(yùn)算量是十分巨大的，為此我們考慮在FPGA上用硬件的方式實(shí)現(xiàn)。

If LBP algorithm is implemented in a software way, it will be very slow.FPGA have features of concurrent computation, reconfiguration and large data throughput.It is suitable to be built an embedded surveillance system.The algorithm introduced above is implemented on a FPGA board.PPT(11)

這就是我們硬件實(shí)現(xiàn)的系統(tǒng)結(jié)構(gòu)圖。首先輸入系統(tǒng)的RGB像素信號(hào)的濾波、灰度計(jì)算及LBP計(jì)算，得到各個(gè)像素塊的LBP值。然后背景更新控制模塊利用幀差模塊的檢測(cè)結(jié)果控制背景緩存的更新。區(qū)域判定模塊根據(jù)背景差模塊的輸出結(jié)果，結(jié)合像素塊的坐標(biāo)信息，對(duì)前景像素塊進(jìn)行區(qū)域判定。

The structure of the system is showed in this figure.In this system, a VGA signal is input to the development board.and the LBP pattern is calculated , Frame difference module also compares the current frame and the previous frame to determine whether there is a moving object in the surveillance vision.If the surveillance vision is static for a certain amount of frame, the background model will be updated.PPT(12)圖中是LBP計(jì)算模塊。圖中所示的窗口提取結(jié)構(gòu)可以實(shí)現(xiàn)3×3像素塊窗口的提取。像素信號(hào)按順序輸入該結(jié)構(gòu)，窗口中的數(shù)據(jù)就會(huì)按順序出現(xiàn)在Pixel1-Pixel9這9個(gè)寄存器中，從而在最短的延時(shí)內(nèi)提取出相鄰9個(gè)像素點(diǎn)的灰度值。行緩存的大小等于每一行圖像包含的像素個(gè)數(shù)減1。將9個(gè)像素點(diǎn)的灰度值通過求均值模塊，可以求出一個(gè)像素塊的像素均值。

將像素塊均值作為輸入再次通過類似的結(jié)構(gòu)，可以提取出3×3個(gè)相鄰像素塊的灰度值。這時(shí)行緩存的大小為每一行包含的像素塊的個(gè)數(shù)減1。再用9個(gè)窗口的灰度值作為輸入，用比較器陣列計(jì)算出最終的LBP值。

To achieve real time computation of the LBP, a circuit structure is put forward as showed in Fig.5.Two line buffers and nine resisters are connected in the way showed in the figure.Nine neighbor pixels are extracted with minimum ['m?n?m?m] delay, and the mean value of this block is calculated by the mean value calculate module which contains some adders and shifters.The mean values of the blocks are inputted to a similar structure and extracted in a similar way, and the LBP is calculated by the consequence LBP calculate module.PPT(13)求均值模塊采用如圖3-12所示的四級(jí)流水方式實(shí)現(xiàn)。在算法的設(shè)計(jì)過程中，需要求出的是3×3像素塊中9個(gè)像素的均值。但是在硬件實(shí)現(xiàn)時(shí)，為了更合理地利用硬件資源，只計(jì)算剔除中心像素后的8個(gè)像素的均值。這樣做可以在不對(duì)計(jì)算結(jié)果造成太大影響的情況下減少加法器的使用。而且在求均值的最后一級(jí)流水，除8運(yùn)算比除9運(yùn)算更容易實(shí)現(xiàn)。因?yàn)?是2的整數(shù)冪，除8運(yùn)算只需要將各個(gè)像素的和右移3位。而除9運(yùn)算在FPGA中需要專用的DSP模塊來(lái)完成。PPT(14)如圖所示，塊均值計(jì)算模塊計(jì)算出的8個(gè)塊均值被圖3-11中的窗口提取模塊提取出來(lái)，并作為比較器陣列的輸入，比較器的輸出結(jié)果用0和1表示。最終的比較結(jié)果按一定的順序排列，重新拼接成一個(gè)8位的二進(jìn)制數(shù)，即LBP值。LBP計(jì)算電路沒有采用流水結(jié)構(gòu)，在一個(gè)時(shí)鐘周期內(nèi)就能得到計(jì)算結(jié)果。

PPT(15)

這個(gè)是在系統(tǒng)測(cè)試中，實(shí)現(xiàn)對(duì)多個(gè)目標(biāo)的檢測(cè)。

In this system test ,we achieve a multi-object detection.PPT(16)

這個(gè)圖是對(duì)動(dòng)態(tài)背景更新的測(cè)試，在監(jiān)控區(qū)域中劃定一個(gè)目標(biāo)區(qū)域，把一個(gè)靜止的物體放置到目標(biāo)區(qū)域中。在前3分鐘內(nèi)，系統(tǒng)會(huì)將其當(dāng)做前景目標(biāo)，矩形窗口會(huì)以閃爍的形式發(fā)出報(bào)警信號(hào)。3分鐘過后，由于物體一直處于靜止?fàn)顟B(tài)，系統(tǒng)檢測(cè)到了10800個(gè)靜止幀，于是更新背景模型。靜止的物體被當(dāng)做背景的一部分，此后窗口不再閃爍。經(jīng)驗(yàn)證，該系統(tǒng)能夠正確實(shí)現(xiàn)背景模型更新算法。

This is the test for the auto background update.We put a statics object in the surveillance area，at the beginning this is trusted as a moving object.after 3 minutes , the system receive ten thousand static frames ,and then update the background model.Then this object is regard as a part of the background.PPT(17)

此外為了驗(yàn)證系統(tǒng)對(duì)室外光照變化抑制能力，我們選取了大量有光照變化，并且有運(yùn)動(dòng)目標(biāo)的視頻對(duì)系統(tǒng)進(jìn)行了測(cè)試。

In order to verify the resistance to the varation of illumination , a certification experiment is designed, and the ROC curves of the two algorithms based on LBP and grayscale are plotted and compared.A number of short video clips with shifty and fixed illumination, including positive samples with moving objects and negative samples without moving objects.PPT(18)

測(cè)試平臺(tái)如圖所示。用一臺(tái)PC機(jī)作為測(cè)試信號(hào)的輸出源，然后在PC機(jī)中播放視頻，并將視頻VGA信號(hào)發(fā)送給運(yùn)動(dòng)目標(biāo)檢測(cè)系統(tǒng)，模擬真實(shí)的監(jiān)控環(huán)境。FPGA將輸入信號(hào)和區(qū)域邊框圖形相疊加后在LCD上顯示。

The picture of the certification experiment is showed in this picture.A PC acts as the source of the test signal which is input to the FPGA in the form of VGA.Passing through the FPGA board, video signal is displayed on a LCD screen.PPT(19)

并最終描繪了系統(tǒng)的ROC特性曲線。在沒有光照強(qiáng)度變化的情況下，采用基于灰度的運(yùn)動(dòng)目標(biāo)檢測(cè)算法的性能略優(yōu)于基于LBP值的運(yùn)動(dòng)目標(biāo)檢測(cè)算法，兩種算法都能取得較好的檢測(cè)效果。但是在圖5-15中（測(cè)試集2），也就是在光照強(qiáng)度變化的情況下，畫面整體灰度發(fā)生較大的改變，基于灰度的檢測(cè)算法的性能大幅度下降，接近于失效。而采用LBP值的檢測(cè)算法卻能維持較好的性能?？梢娀贚BP的檢測(cè)算法對(duì)抑制光照強(qiáng)度變化造成的誤檢測(cè)有較好的效果。

This two figure are the ROC curves of the experiments using our

algorithm and traditional grayscale-based algorithm.We can see in the Fig.1 which corresponds to the condition with fixed illumination, the performance of the grayscale-based algorithm is slightly better than these of LBP-based algorithm, they can both detect moving object effectively.But in Fig.2 which corresponds to the condition with shifty illumination, grayscale based algorithm deteriorates drastically and nearly lose efficacy ?k?s?].But the improved LBP algorithm still keeps a good performance.PPT(20)

謝謝大家！

Thanks for your attention

第三篇：計(jì)算機(jī)國(guó)際會(huì)議英文開幕詞

An opening speech

Ladies and gentlemen, It is a great pleasure and honor for me to declare the opening of the International Conference on Computer.First of all, on behalf of the university, I would like to extend a warm welcome to all of you.I do hope you will enjoy your stay here.Secondly, the opening of the International Conference on Computer contributes to the development of computer science in our university as well as the communication and cooperation among all the scholars in this field.Last but not least, I would like to express my sincere good wishes for a successful conference.Thank you very much.

第四篇：英文國(guó)際會(huì)議主持人稿（寫寫幫推薦）

Opening remarks: Distinguished Delegates and Guests,Ladies and Gentlemen,it’s a great privilege for me to start the conference.Let me introduce myself first.I am Du Ruimin from Harbin Engineering University.And I am very honored to be the chair person for this morning’s session.It is a great pleasure for me to share the chairmanship with Professor Lee Guobin who is Harvard University.On behalf of the organizing committee of TCASSP , I would like to announce the session open.What we are going to do this morning is to review the different aspects of signal processing and their current research challenges.We have some of the world’s foremost professors and researchers, people at the forefront of this field.Let me introduce our first speaker Professor Lee Guobin, who is the Director of Information and Communication Engineering apartment of Harvard University.Professor Lee has published extensively in SCI and books on the subject of Image, Video, and Multidimenional Signal Processing.His presentation is entitled “No-reference perceptual quality assessment of JPEG compressed images”.Let’s welcome professor Lee~ Thank you, Prof.Lee.Your presentation is very convincing.From your presentation, we know that(---)Your speech is indeed very useful, interesting and challengeable.Thank you.Q&A--------------------OK, let me introduce the next speaker Prof.Dai Jia.Professor Dai jia comes from Columbia University who is famous for his study on Signal Processing Theory and Methods,and also make its application have a Practical significance.Our speaker is also co-author of five books and over 40 published articles.As a communications expert, he has been quoted in the Seattle Times, the Chicago Tibune and the Atlanta Journal Constitution.Now a lot of first-class books on this subject are wrote by Professor Dai,and today we are very honored to have Prof.Dai give us a speech entitled “Fractional Fourier Transform and Its Applications”.Let’s welcome Prof.Dai.Q&A--------------------Thank you, Prof.Dai.Your speech is the absolutely inspiring.We are delighted to be able to share your new specific strategies and techniques.(----)will be greatly cherished by the people present here.Now, let’s welcome our next speaker, Dr.Cao Qingming.Dr.Cao Qingming is a professor and the chairperson of the Electrical Engineering Department at the Ohio State University in Columbus, Ohio.Our speaker got his ph.D.in EE at the University of California, Berkeley, followed by a series of teaching and research positions at Harvard, Cambridge University, and Princeton.For the past 6 years,he published more than10 papers on journal.Please join me in welcoming our guest speaker today—Dr.Cao Qingming, whose topic is entitled Signal Processing for Communications and Networking.Q&A--------------------

（Thank you very much for your worthwhile/ enlightening/informative presentation.Let’s welcome the next speaker Prof.Guo Xiangchen with warm applause.）Prof.Guo Xiangchen is from Chongqing Jiaotong University,who is co-author of five books and over 40 published articles.As a communications expert, he has been quoted in the Seattle Times, the Chicago Tibune and the Atlanta Journal Constitution.Our speaker has been honored many awards--2013 Marconi Prize Paper Award and a national Book Award.Today, Guo Xiangchen will address you on Multicast Scheduling and Resource Allocation Algorithms for OFDMA-Based Systems: A Survey.Let’s welcome Prof.Guo Xiangchen.Q&A--------------------

Prof.Guo Xiangchen’s speech is highly useful, interesting and informative.We have learnt a lot from him.Thank you again, Prof.Guo Xiangchen.Q&A Ladies and Gentlemen, our distinguished guest speakers have finished their presentations.We now enter into discussion and share with each other our different ideas.I hope that all here present will feel free to express your ideas and exchange various opinions, so as to make this discussion a real success.Yes, the young man in the second row, please.Closing speech:

I’d like to pay my tribute to the speakers for their excellent presentations and the audience for their attention this morning.I declare the plenary session adjourned until 12 a.m.

第五篇：高校舉辦國(guó)際會(huì)議英文開幕詞

Opening Remarks

ON CME2011 Distinguished Guests and Leaders, Ladies and Gentlemen, Dear Friends, It is an honor and a pleasure to welcome you on behalf of the Automation College of Harbin Engineering University to this conference on Complex Medical Engineering.Firstly thank every one of you for attending.Many of you have travelled great distances to be here.With excellentspeakers attending from dozens of countries, this conference is a truly global event.We are glad to see that hundreds of participants from overseas including United States, Canada, Italy, Australia, United Kingdom, France, Germany, Japan, Korea ,Etc.andmore than fifty professionals from mainland chinaincluding Taiwan and Hong Kong have come to this event.Let me now turn to introduction of Automation College of HEU.The history of the Automation College can be traced back to 1953, when Harbin PLA Military Engineering Institute was founded.The College is being developed and strengthened through its core disciplines of 'Ocean Tunnel Measuring' and 'Ship-Electricity'.The College is one of the teaching and research backbones of HEU.There are 6 undergraduate programs including Automation, Measuring and Control Technology and Instruments, Electrical Engineering and its Automation, Detection Guidance and Control Technology, Biomedical Engineering, and Aviation Technology;16 master programs including Control Theory and Control Engineering, Navigation, Guidance and Control, Pattern Recognition and Intelligent Systems, Detection Technology and Automatic Equipment, Electric Machines and Electric Apparatus, Power System and its Automation, Precision Instrument and Machinery, Biomedical Engineering, Mechantronic Engineering, Systems Engineering, System Analysis and Integration, High Voltage and Insulation Technology, Power System and its Automation, Power Electronics and Power Drives, Traffic Information Engineering & Control, and Detection and Measuring Technology and Instrument;6 doctoral programs including Control Theory and Control Engineering, Navigation, Guidance and Control, Precision Instrument and Machinery, Detection Technology and Automation Equipment, Pattern Recognition and Intelligent Systems, and System Engineering.In addition, the nationally top ranking discipline of Control Science and Engineering has a Postdoctoral R&D Base.In 2001, the discipline of Navigation, Guidance and Control was appraised as a national key discipline.During the process of prejudication and proposal of the National 10th Five-years Plan and the '211 Project', Control Theory and Control Engineering and Navigation, Guidance and Control are listed as the key disciplines.To be internationally leading, the College has invested 23 million yuan RMB for the two disciplines' further construction.Finally ,you are sincerely invited to come to Harbin Engineering University and I wish the conference a complete success!