第一篇：模擬國(guó)際會(huì)議主持人講稿

Ladies and Gentlemen, I have the honor and the pleasure ,on behalf of the Organizing Committee ,to extend our cordial welcome to all the scientists and experts who have been invited to this internationalconferenceof “onebeltandoneroad“.Let me introduce myself.I am hujianjun, from Tsinghua University.It is a greate pleasure for me to be the chairman of the session today.When Chinese President Xi Jinping visited Central Asia and Southeast Asia in September and October of 2013, he raised the initiative of jointly building the Silk Road Economic Belt and the 21st-Century Maritime Silk Road, which have attracted close attention from all over the world.The Belt and Road Initiative is a way for win-win cooperation that promotes common development and prosperity and a road toward peace and friendship by enhancing mutual understanding and trust, and strengthening all-around exchanges.Now A series of results in different field have been obtained.The Congress will cover all aspects of One Belt and one Road，and the strategy , impact ,achievement of B&R will be included.I hope that the conference will improve our understanding of OBOR, and I also hope that the congress will provide the opportunity for personal exchange of scientific results ,facilitate the making of new acquaintances and to strengthen the personal friendships among participants from different parts of the world.Today’s speakers will share their thoughtson “onebeltandoneroad”.Then, we’ll have a Question and Answer session, which allows the everyone to ask some questions you may be interested.I am sure that you will find some topics to be presented both interesting and informative.下一環(huán)節(jié)

now,let’s go on the first stage of the conference ,which is about making an oral presentation about onebeltandoneroad.介紹演講者

1.Now, I will be very excited to introduce the firstspeaker, Prof.XXX, who is a professor of political science at the Peking University.And his topic is “The One Belt And One Road: Actions and Future.”

2.Let me introduce the second speaker, who is very very rich, not in dollars, but in knowledge and experiences, She got her Ph.D in the economics ,Central SouthUniversity, followed by a series of teaching and research positions at OxfordUniversity.Please don’t hesitate to join me in welcoming our first speaker, Prof.XXX, whose topic is（“The Security Challenges to the “One Belt and One Road” Strategy and China's Choices “）.Welcome.3.Let’s welcome to professorXXX，whoisa Sociologist from Tsinghua University.He will give us a wonderful speech titled “Belt and road“--to promote China’s Marshall plan”.4.Today our 4th speaker is XXX, professor ofChicago University，The title of her presentation is “The“One Belt and One Road”:the Bridge between the Chinese Dream and the World Dream”.Let’s welcome to professor于彥芳.5.Our 5th speaker is XXX , as the president of Baidu company, he will give us an excellent report titled “The One Belt and One Road Grand Smart Banlance”.Let’s welcome to president yang with our warm applause.6.OurnextspeakerisprofessorXXX comefromColumbia University，whosetopictiteld“Examining the ＂One Belt and One Road＂Strategy in the Background of RMB Internationalization“，Let’s welcome to professorXXX.7.Today our 7th speaker is XXX, who is aExperienced professorofColumbia University, The title of her presentation is “Impact of the National Strategy of One Belt and One Road for World Investment Construction”.Let’s welcome to professorXXX.8.I will be very pleased and excitedtointroduceour8speaker，王

th歡，the director of National Energy Administration，andthetitleofhistopicis“＂Belt and Road＂Initiative Promote International Energy Cooperation“.9.OurlastspeakerisXXX，professorofFudanUniversity,will give us an excellent report titled”O(jiān)ne Belt, One Road" Initiative: The Impacts on Economy of China’s Various Region

第二環(huán)節(jié)，討論環(huán)節(jié)。

Ladies and gentleman, our distinguished speakers have finished their presentations, we now enter into the question and answer session.Isthereanyquestionyouwouldliketoaddresstoourspeakers.I believeourspeakerwillgiveyouanysatisfactoryanswers。Anyquestionplease? Any additional questions.? I hope the audience will participate in the discussion by rising their hands.Nobody, ok ,Iwould like ask professor 蔣 a question.””

提問(wèn)：

1.what kind of opportunity the implementation of B&R can bring to college students? 2.what can we do and how to do in order to achieve self-value better when the country drive the B&R? 3.how does the construction of B&R make our country more influential?

控制進(jìn)程：

Let’s keep on schedule and go ahead to the fourth paper.We’ll then go on with the last paper.Well, I am sure we could discuss longer, but unfortunately time is up.Thank you very much, Dr A.Our next speaker isDr C.Sorry, we don’t have any time for questions, so we have to proceed to the next paper.即將結(jié)束，時(shí)間限制

Okay, well, as time is limited.I am sorry to say that this session will have to stop here.Now ,let’s welcome the chairman ,Miss Fu to give us a Closing speech.Closing speech First of all, I would like to thank Mrs.Make and the organizing committee for having appointed me to serve as the chair of this conference.We are now very close to the end of this conference.I believe that our conference is a great success.More than 40 participants have come to Changsha to discuss not only general questions about one belt and one road but more concrete questions and problems and their possible solutions.It went smoothly as scheduled.In these two days the conference has covered so many important and complex problems in the flied of one belt and one road both theoretical and practical.All the presentations were very illuminating and informative.And the heated panel discussions were very stimulating and fruitful.It’s our hope that the result of the conference will carry the study of one belt and one road to a new stage.We all hope to maintain close contact and cooperation with each other in the field of future research work on one belt and one road.Now, with great joy and reluctant mind to part, we get together again to declare that the conference has drawn to a successful close.Thank you to every body who has contributed to the conference with reports and introductions。

As the chair of the conference, I would like to express my thanks again.Thank you for coming to the 2st international conference of one belt and one road.

第二篇：英語(yǔ)模擬國(guó)際會(huì)議講稿

英語(yǔ)模擬國(guó)際會(huì)議

主持人：王×?xí)h出席人：朱××?xí)h中提問(wèn)者兩人：董×、趙××休會(huì)中途與嘉賓交談兩人：張×、唐×× 會(huì)議結(jié)束提問(wèn)者：余×、龔× 主持人王×：Ladies and Gentleman: May I have your attention please? Our conference will begin in a few minutes.All the presenters are requested to be seated.Let me introduce myself,i am wang yuan from SCNU,it’s a privilege for me to chair this session.Once the ceremony has started,you are refrained from taking pictures, using flashbulbs or leaving your seats.3Q.Distinguished guests, distinguished delegates, ladies and gentlemen, and all the friends:At this special time of wonderful June, in this grand hall of the beautiful city, our respectable guests are here getting together.Academic Seminars of CAS are organized by the Bureau of Personnel and Education of CAS, and held by the CAS research institutes.Now, first of all, please allow me to give our hearty welcome to all of you present, and thank you, for your friendly coming.We feel so proud, and appreciated as well to be the host of the event.For this conference, we are following the agenda here.The meeting is supposed to last for five days，it is the first congress which covers the true sense of psychological education、moral education,basic education and higher education, application education fields.And it to be separated into two parts, to begin with, we’ll invite some representatives from our guests to give lectures about their latest researches and reports on the issue, and then we will have some symposiums.And finally I wish you an unforgettable and prefect experience here.Firstl,i’d like to introduce our first presenter,Professor Jan.She is the author of “cooperation and competition”.for the past six years,JAN has been honored many awards--a Pulitzer Prize winner, a national Medal of the economy and a National book award and so on.Now, please join me in welcoming our guest speaker today---JAN.,whose topic is “cooperation can improve our competitive”.發(fā)言人朱××：

Good morning!Mr.Chairman, your excellencies , fellow colleagues ,Ladies and Gentlemen!Firstly, i would like to thank zhuxiaoli for her gracious introduction.I am very glad to have this opportunity of sharing with you our view on cooperation.My topic of today is“ cooperation can improve our competitive ”.As we all know, competition is a common phenomenon in our society.It occurs in almost every field of our life, such as playing games, doing our study, hunting for jobs.As I stand in here with other Participants ,it’s also a fierce competition.Working hard at something and competing against others can inspire us to push ourselves further than we otherwise might.In other words, competition is required to prompt us to excel and to help us reach our fullest potential.Last of all, competition is seen as an open and fair race where success goes to the swiftest person regardless of his or her social backgrounds.We can say, in this sense, competition stimulates people's interest in work and helps society to go forword.However, as the wave of globalization has come and the development of society, we face more competitions from the outside world.Are we going to face the challenge all by ourselves alone? The answer is clear,Human beings are social beings and no one can exist alone in the society.If you want to play the game well, you have to play with others.You cannot play single-handed and win.You’ll always have to cooperate with your partners, who may make the social ladder for you to climb to the top.From cooperation, you build up trust and understanding, which does good to your future.And also it’s said that we get together to do something larger than one single person, that is to say ,cooperation can turn a small business into a big and strong one.You see,after the cooperation with IBM.Lenovo could challenge the Dell computer company as the world NO.2 PC maker, BenQ and SIMENS mobile, Sony and Ericssons, the two groups of companies are collaborating together to win more market.Everyday, there are over 10 thousand companies annexed because of the crucial competition,but there are collaborating together in order to acquire more competitive ability.From a whole nation’s aspect, all the nations should take the national interest as a common goal.Take China and India for example.India, along with Japan, is a main rival of China in Asia.For the history’s sake, India and China have already competed with each other for a long period of time.With the globalization’s steps getting faster, both China and India realize the importance of cooperation.Now they have already started collaborating in the field of IT and mineral exploitation, and the two countries have benefited a lot.As American previous president Bill Clinton ever said:“There are no forever friends nor rivals, but interest.”So, if situation changes, competition also could turn into cooperation.To sum up, competition and cooperation prevail throughout the world.We should, however, take advantage of the competition as a chance to promote the cooperation and finally be the winner in the competition.we should seek cooperation boardly to improve our competitive.That’s of my speech..Thank you very much, ladies and gentlemen.主持人王×：3Q, Dr.Jan.I think all the participants present here this morning will agree with me that your presentation is very informative and enlightening.Now, do anybody have some questions？

提問(wèn)1號(hào)趙××：（麻煩想個(gè)問(wèn)題，關(guān)于競(jìng)爭(zhēng)與合作的）提問(wèn)2號(hào)：董×：（麻煩想個(gè)問(wèn)題，關(guān)于競(jìng)爭(zhēng)與合作的）主持人王×：上半段時(shí)間到了，請(qǐng)大家休息10分鐘，10分鐘后會(huì)議繼續(xù)。

課間：提問(wèn)三號(hào)張×和唐×一起去喝水，在打水的地方碰到發(fā)言者朱×，然后開始對(duì)話。。

張×： 唐×： 朱×： 主持人王×：時(shí)間到了，大家安靜就座。現(xiàn)在有請(qǐng)朱××給大家做一個(gè)總結(jié)。朱××：總結(jié)幾句就可以 主持人：還有什么問(wèn)題嗎？ 提問(wèn)者3號(hào)龔×：（想個(gè)問(wèn)題）提問(wèn)者4號(hào)余×：

（想個(gè)問(wèn)題）

第三篇：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(méi)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(méi)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。基于灰度的算法在光照條件改變的情況下，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(wèn)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(wèn)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(jiàn)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(méi)有改變，LBP值也沒(méi)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(wèn)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(méi)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(wèn)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(guò)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(guò)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(guò)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(méi)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(guò)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(méi)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。可見(jiàn)基于LBP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

第四篇：模擬國(guó)際會(huì)議演講稿

1.Introduction Thank you very much.Mr.Jiao, for your kind introduction.Ladies and gentlemen, Good afternoon!My name is Lijia, came from Harbin Engineering University.I am honored to have been invited to speak at this conference.Before I start my speech, let me ask a question.Do you know what can affect the properties of foam concrete? Do you think how to reinforced the properties of foam concrete？Most of the investigations on foam concrete in the past have been con?ned（被限于）to neat cement paste, cement paste with partial replacement with admixtures and to cement–sand mixes.Today, my topic is about In?uence of ?ller type on the properties of foam concrete.I want to share our interesting research result on reinforced concrete frame with you.The content of this presentation is divided into 4 parts: In section 1, I will introduce what is the foam concrete.In section 2, I will talk about Parameters investigated and mix compositions.In section 3, I will give E?ect of water–solids ratio on design density.And finally, I will make a conclusion.2.Body Section 1: the foam concrete Now, I will introduce the foam concrete.Pre-formed(成型的)foam concrete is manufactured（加工）by adding foam, prepared by aerating（充氣）a foaming agent solution, to cement paste or cement mortar（灰

漿）.The composition（合成物）, physical properties（性能）and uses of foam concrete were discussed in detail（詳細(xì)的）by Valore, Short and Kinniburgh, Rudnai and Taylor.Although several investigations have been conducted on the properties of foam concrete, most of them deal with cement–sand mixes, neat cement paste with or without partial replacement（局部替換）using admixtures（摻合料）.Few studies report on the in?uence of ?ller type on the properties of foam concrete.By using ?y ash（粉煤灰）as ?ller(?ne aggregate細(xì)骨料)instead of sand, the high volume（體積）utilization（利用）of ?y ash becomes possible可能, thus providing a means of eco-nomic（經(jīng)濟(jì)）and safe disposal（處理）of this waste product.Comparison（比較）of strength of air-cured foam concrete made with cement-sand and cement–?y ash for masonry（砌體結(jié)構(gòu)）by Durack and Weiqing show that for products of comparable density（比較密度）, mixes with ?y ash as ?ne aggregate in place of sand gave relatively higher strength.Section 2: Parameters(參數(shù))investigated and mix compositions（組成成分）

So much for the foam concrete, now I will move on to Parameters(參數(shù))investigated and mix compositions.As the experimental programme(實(shí)驗(yàn)程序)was aimed at studying the e?ect of the ?llers on the properties like density（密度）, ?ow behaviour（流動(dòng)特性）, water absorption（吸水率）and strength of foam concrete, the following mixes

were investigated by keeping the basic ?ller–cement ratio constant（恒定不變）at 1:1 by weight.The foam required for three densities（密度）of foam concrete viz.1000, 1250, 1500 kg/m3 were arrived at as per ASTMC 796-97.In the cement–sand–?y ash mixes 50% of the sand is replaced with ?y ash and in the cement–?y ash mixes all the sand is replaced with ?y ash.Section 3: E?ect of water–solids ratio（水砂比率）on design density That bring me to E?ect of water–solids ratio（水砂比率）on design density.I think this part is the most important in my presentation, I will explain in detail.As the foam is added to the wet foam concrete mix, the consistency（稠度）of the wet mix is very important to get the design density.Fig.2(a)and(b)show the variation of density ratio（密度變化率）(measured fresh density divided（分離）by design density)with water–solids ratio for mixes with di?erent ?ller type for each of the design densities, viz., 1000 and 1500 kg/m3, respectively（分別地）.It is observed that at lower water–solids ratios, i.e., at lower consistency, the density ratio is higher than unity（個(gè)體）.The mix is too sti?（嚴(yán)格地）to mix properly thus causing the bubbles（氣泡）to break during mixing resulting in increased density.At higher water–solids ratios there is also an increase in density ratio as higher water contents make the slurry（泥漿）too thin to hold the bubbles resulting in segregation（分離）of the foam from the mix along with segregation of the mix itself thus causing

an increase in measured density.Therefore, as shown in Fig.2(a)and(b), a density ratio of unity or nearly unity is achieved only at a particular consistency.This consistency requirement for the mix before adding foam to it can be expressed in terms of water–solids ratio.It is also observed that the water–solids ratio required to obtain a density ratio value of one, depends on the ?ller type.Section 4: Conclusion The conclusions drawn from this study and summarized below are applicable（合適的）to the characteristics of the materials（材料特性）used and the range of parameters（參數(shù)范圍）investigated:(i)the consistency of pre-formed foam concrete mixtures(de?ned as the water–solids ratio for achieving the target（目標(biāo)）density)mainly depends on the ?ller type, i.e., relatively higher for mixes with ?y ash as ?ller compared to mixes with sand;(ii)the ?ow behaviour mainly depends on the foam volume and as the foam volume increases the ?ow decreases.For a given density, foam concrete with ?y ash as ?ller showed relatively（相當(dāng)?shù)模﹉igher ?ow values;(iii)for a given density, an increase in ?y ash content of the mix results in increased strength.In comparison（比較）to cement–sand mixes, cement–?y ash mixes showed relatively higher water absorption（吸收）.That’s all.Thank you!Are there any questions?

The picture on this slide is

So much for......, now I will move on to......This slide shows the calculation result

As we know, the target user usually has taken a list of courses rather than a course, so we need to extent our probability calculation formulars.For example, suppose T={a,b}, Pr[x﹁T] the probability x occurs without either an a or b preceding it;Pr[x﹁T] the probability x occurs without either an a or b following it.This probability can be calculated exactly.So how to calculate it?

That bring me to Recommendation Algorithms.I think this part is the most important in my presentation, I will explain in detail.In conclusion, we proposed a novel precedence mining model, developed

To sum up, first, I introduced the motivation and the outline of work;second, I gave the definition of precedence mining model;third, I described some new recommendation algorithms using precedence information;forth, I showed our experimental results to compare the new algorithms with traditional ones.Finally, I made a conclusion of our work..That’s all.Thank you!Are there any questions?

第五篇：模擬國(guó)際會(huì)議演講稿

Recsplorer:Recommendation Algorithms Based on Precedence Mining

1.Introduction Thank you very much, Dr.Li, for your kind introduction.Ladies and gentlemen, Good morning!I am honored to have been invited to speak at this conference.Before I start my speech, let me ask a question.Do you think recomemdations from others are useful for your internet shopping? Thank you.It is obvious that recommendations play an important role in our daily consumption decisions.Today, my topic is about Recommendation Algorithms Based on Precedence Mining.I want to share our interesting research result on recommendation algorithms with you.The content of this presentation is divided into 5 parts: in session 1, I will intruduce the tradictional recommendation and our new strategy;in session 2, I will give the formal definition of Precedence Mining;in session 3, I will talk about the novel recommendation algorithms;experimental result will be showed in session 4;and finally, I will make a conclusion.2.Body Session 1: Introduction The picture on this slide is an instance of recommemdation application on amazon.Recommender systems provide advice on products, movies,web pages, and many other topics, and have become popular in many sites, such as Amazon.Many systems use collaborative filtering methods.The main process of CF is organized as follow: first, identify users similar to target user;second, recommend items based on the similar users.Unfortunately, the order of consumed items is neglect.In our paper, we consider a new recommendation strategy based on precedence patterns.These patterns may encompass user preferences, encode some logical order of options and capture how interests evolve.Precedence mining model estimate the probability of user future consumption based on past behavior.And these probabilities are used to make recommendations.Through our experiment, precedence mining can significantly improve recommendation performance.Futhermore, it does not suffer from the sparsity of ratings problem and exploit patterns across all users, not just similar users.This slide demonstrates the differences between collaborative filtering and precedence mining.Suppose that the scenario is about course selection.Each quarter/semester a student chooses a course, and rates it from 1 to 5.Figure a)shows five transcripts, a transcript means a list of course.U is our target student who need recommendations.Figure b)illustrates how CF work.Assume similar users share at least two common courses and have similar rating, then u3 and u4 are similar to u, and their common course h will be a recommendation to u.Figure c)presents how precedence mining work.For this example, we consider patterns where one course follows another.Suppose patterns occour at least two transcrips are recognized as significant, then(a,d),(e,f)and(g,h)are found out.And d, h, and f are recommendation to u who has taken a, g and e.Now I will a probabilistic framework to solve the precedence mining problems.Our target user has selected course a , we want to compute the probability course x will follow, i.e., Pr[x|a].﹁howerve, what we really need to calculate is Pr[x|aX] rather than Pr[x|a].Because in our context, we are deciding if x is a good recommendation for the target user that has taken a.Thus we know that our target user’s transcript does not have x before a.For instance, the transcript no.5 will be omitted.In more common situation, our target user has taken a list of courses, T = {a,b,c,…} not

﹁just a.Thus, what really need is Pr[x|TX].The question is how to figure out this probability.I will answer it later.Session 2: Precedence Mining We consider a set D of distinct courses.We use lowercase letters(e.g., a, b, …)to refer to courses in D.A transcript T is a sequence of courses, e.g., a-> b-> c-> d.Then the definition of Top-k Recommendation Problem is as follows.Given a set transcripts over D for n users, the extra transcript T of a target user, and a desired number of recommendations k, our goal is to: 1.Assign a score score(x)(between 0 and 1)to every course x ∈ D that reflects how likely it is the target student will be interested in taking x.If x ∈ T , then score(x)= 0.2.Using the score function, select the top k courses to recommend to the target user.To compute scores, we propose to use the following statistics, where x, y ∈ D: f(x): the number of transcripts that contain x.g(x;y): the number of transcripts in which x precedes course y.This slide shows the calculation result of f(x)and g(x,y).For example, from the table, we know that f(a)is 10 and g(a,c)is 3.We propose a precedence mining model to solve the Top-k Recommendation Problem.Here are ﹁some notation: xy, which we have memtioned in session 1, refers to transcript where x occurs without a preceding y;x﹁y refers to transcript where x occurs without y following it.We use quantities f(x)and g(x,y)to compte probabilities that encode the precedence information.For instance, from formular 1 to 7.I would not tell the detail of all formulars.We just pay attention to

﹁formular 5, note that this quantity above is the same as: Pr[x﹁y |yx] which will be used to compute score(x).As we know, the target user usually has taken a list of courses rather than a course, so we need to

﹁extent our probability calculation formulars.For example, suppose T={a,b}, Pr[xT] the probability x occurs without either an a or b preceding it;Pr[x﹁T] the probability x occurs without either an a or b following it.This probability can be calculated exactly.So how to calculate it?

Session 3: Recommendation Algorithms Let’s review session 2.The main goal of the recommendation algorithms is to calculate the score(x), and then select the top k courses based on these scores.Traditional recommendation algorithms compute a recommendation score for a course x in D only based on its frequency of occurence.It does not take into account the courses taken by the target user.Our recommendation algorithms called SingleMC conquer the shortcoming of the traditional ones.It computes the score(x)using the formular 5.The detail is as follows: a student with a transcrip T of taken courses, for the course y ∈ T, if y and x appear together in transcripts satisfies the

﹁threshold θ, then compute the Pr[x﹁y |yx], reflecting the likelihood the student will take course x

﹁and ignoring the effect of the other courses in T;finally the maximum of Pr[x﹁y |yx] is choosen as the score(x).Here is the calculation formular of score(x)of SignleMC.For example, with the higer score, d will be recommended.Another new recommendation algorithm named Joint Probabilities algorithm, JointP for short, is proposed.Unlike SingleMC, JointP takes into account the complete set of courses in a transcript.In formular 12, we cannot compute its quantity exactly, Remember this problem we have mentioned.Our solution is to use approximations.This slide is about the first approximating formular.And this the second approximating formular.The system is courseRand, and data set for experiment contains 7,500 transcripts.This slide shows the new recommendation algoritms with black color and the traditional ones with blue color.The chart on this slide indicates our new recommendation algorithms beat the traditional ones in precision, because the former ones exploit patterns across all users, while the latter ones just use the similar users.The chart on this slide points out our new recommendation algorithms also beat the traditional ones in coverage for the same reason.Session 5: Conclusion and Summary In conclusion, we proposed a novel precedence mining model, developed a probabilistic framework for making recommendations and implemented a suite of recommendation algorithms that use the precedence information.Experimental result shows that our new algorithms perform better than the traditional ones, and our recommendation system can be easily generalized to other scenarios, such as purchases of books, DVDs and electronic equitment.To sum up, first, I introduced the motivation and the outline of work;second, I gave the definition of precedence mining model;third, I described some new recommendation algorithms using precedence information;forth, I showed our experimental results to compare the new algorithms with traditional ones.Finally, I made a conclusion of our work..That’s all.Thank you!Are there any questions?