Research of chemical exchange saturation transfer in brain

化学交换饱和转移(chemical exchange saturation transfer,CEST)成像是在磁化传递及化学交换理论基础上发展起来的一种磁共振成像新方法,其扩展了磁共振分子影像新领域,但还处于研究阶段。其以细胞内物质为内源性对比剂,通过水信号间接检测代谢物信息,进行组织的酸碱度成像及其各种代谢物成像。本文主要探讨MRI领域中与水相关的化学交换饱和转移现象,阐述其原理、研究现状及其在不同场强磁共振仪上脑部疾病的应用。Chemical exchange saturation transfer(CEST) imaging is a new method for magnetic resonance imaging theory of exchange in the magnetization transfer and chemical, the expansion of the new field of molecular magnetic resonance imaging,but it's still in the research stage. The intracellular substances as an endogenous contrast agent, through the indirect detection of metabolite water signal information for tissue p H imaging and imaging of various metabolites. This paper mainly discusses the chemical and water exchange in the field of MRI saturation transfer phenomenon,expounds the principle, research status and the application in brain diseases used the different field strength clinical MRI scanner.2014年厦门市科技局科技惠民计划项目(编号:3502Z20144052)~

Response of Prorocentrum minimum growth to zinc limitation

研究了低中高 3种Zn2 + 浓度下 ,赤潮藻微小原甲藻的生长和生理响应 .结果表明 ,低Zn(1.4pmol·L-1)下 ,藻细胞的比生长速率和稳定期生物量分别为 0 .4 0d-1和 5 110 0cell·ml-1.当Zn2 + 浓度超过2 4 .4pmol·L-1时 ,提高Zn2 + 浓度 (181.6pmol·L-1) ,藻细胞的比生长速率没有改变 ,为 0 .93d-1,而稳定期生物量则略有下降 ,但均明显高于低Zn条件下藻细胞的比生长速率和稳定期生物量 .Zn限制条件下藻细胞的叶绿素a合成受到影响 ,藻细胞光合作用需在更高光强下达到饱和 .随着Zn2 + 浓度增加藻细胞光饱和的光合作用速率 (Pm)及光合作用效率 (α)均明显增大 .研究表明 ,富营养化水体中 ,高的Zn浓度是一定条件下触发赤潮藻类爆发性增殖的重要因子之一 .Studies on the growth and physiological response of red tide alga Prorocentrum minimum to three Zn 2+ levels were showed that the specific growth rate and biomass were limited in low Zn 2+-grown cells (1.4 pmol·L -1, which were 0.40 d -1 and 51100 cell·ml -1 respectively. The specific growth rate was not significantly different when the Zn 2+ concentration in medium was increased over 24.4 pmol·L -1,but there was a slight decrease in biomass; however, both specific growth rate and biomass were much higher than those in low Zn 2+-grown cells. It was also showed that chlorophyll a synthesis was limited due to Zn 2+ deficient,and therefore,the cells became light saturated at higher irradiance under Zn-limited condition. Light-saturated photosynthetic rates (Pm) and photosynthetic efficiency (α) increased significantly with increasing Zn 2+ concentrations. It was concluded that Zn 2+ concentration might be one of the key factors affecting red tide blooms in eutrophication environment.国家重点基础研究发展规划项目 ( 2 0 0 1CB40 970 6);; 国家自然科学基金资助项目 ( 2 0 1760 60 )

A comparative study of characteristics of higher vocational colleges— take Community college in the United States, Short-term university in Japan and Private vocational college in China for example

本世纪以来，高职院校的数量伴随着我国等教育规模的快速扩张而不断增长，成为我国高等教育的重要组成部分，并在培养高素质技术技能人才方面扮演着重要角色。但是，受经济、政治、文化等诸多因素的影响，尤其是在经济发展进入新常态、产业结构升级转型的背景下，我国高职院校在办学过程中的弊端逐渐显露出来，主要表现在：高职教育整体定位模糊，自身特色不强；办学经费保障机制不健全；办学质量不高，等等。尤其是民办高职的教育质量问题更为突出，因此，本论文主要集中研究民办高职的办学问题。 在对我国民办高职的办学问题进行社会科学分析时，运用历史与比较的视角是十分重要的。美国社区学院应社会经济发展对技术人才的迫切需要而诞生，并...Since the beginning of this century, higher vocational colleges have been growing with the rapid expansion of China's higher education, which has become an important part of higher education in China and plays an important role in cultivating high-quality technical and technical talents. However, under the influence of many factors such as economy, politics and culture, especially in the backgroun...学位：教育学硕士院系专业：教育研究院_高等教育学学号：2572013115182

论情绪与情感的动机作用

情绪和情感一直是心理学研究的重点,本文试图通过动机视角来剖析情绪与情感产生的原因和目标指向,并且着重研究害羞这一情绪,试图从社会文化心理等方面解释害羞这一个“东方情绪“

Honeypot based VoIP防禦系統

[[abstract]]隨著網路普及化，透過網路交談( Voice over Internet Protocol )變成了一種新的通訊趨勢，通訊變得更加便利，也進而降低通訊成本；Session Initiation Protocol (SIP) (RFC3261)負責控制通話建立的訊令，由於SIP是應用層的通訊協定，存在著不少的弱點與威脅，因此安全防範的工作就日益重要，目前大部分運用一些現有的方法：HTTP digest, Transport Secure Layer (TLS), SIPS, IP security (IPsec) 及 Secure MIME(S/MIME)等達到系統的安全性。 除了利用現行的技術之外，入侵偵測系統( Intrusion Detection System )可以提供管理者在攻擊展開攻擊之前，發出警訊，提早做好防備。本研究將VoIP相關的攻擊，以Attack Tree分類表示，並利用Honey-pot的概念，藉由刻意部署服務與攻擊者之間的通訊，收集惡意攻擊者的資訊，以彌補入侵偵測系統在收集資訊的不足。以期有效減少SIP服務因遭受攻擊而導致損害的程度

以 FM-index 為基礎之第三代定序自我型錯誤修正法;A self-error correction algorithm for third-generation sequencing using FM-index

[[abstract]]因為第三代定序技術所產生出的序列為較長的序列，定序的偏差也較低還有定序分布平均等特質，使得第三代定序技術成為現有基因組裝(de novo assembly)的受歡迎選項。 但是由於它所產出的序列錯誤率較高，所以在進行基因組裝前都先必須進行序列的錯誤修正。目前錯誤修正的方法可以分為比對序列分析法和非比對序列分析法。比對序列分析法比較費時但可以在高相似度和低覆蓋率的區域修正。另一方面，分比對錯誤修正法比較快速但敏感度較低。在這篇論文裡，我們研發出一個新的非比對錯誤修正法，藉由FM-index試著把錯誤修正問題轉化成路徑搜尋問題。為了能夠在高相似度和低覆蓋率的區域進行錯誤修正，研發出了使用多種長度子字串的可適性種子搜尋演算法。最後實驗結果指出我們的方法比現有的比對序列分析法和非比對序列分析法還要快在大腸桿菌跟酵母菌之下。在大物種線蟲我們的方法比現有的比對序列分析法還要慢但還是比現有的非比對序列分析法還要快速。 The 3rd-generation sequencing technologies are becoming the popular choice in de novo assembly projects, because of long reads, less sequencing bias, and more uniform coverage. But it comes at the cost of much higher error rates and thus error correction is often performed prior to assembly. Currently, error correction methods can be divided into alignment-based and alignment-free approaches. Alignment-based methods are more time-consuming but able to correct reads in repetitive and low-coverage regions. On the other hand, alignment-free methods are much faster but have less sensitivity. In this thesis, we develop a novel alignment-free algorithm which reduces the correction problem to a path-searching problem via FM-index extension. In order to correct reads in low-coverage and repetitive regions, an adaptive seeding algorithm using multiple sizes of k-mers is developed. The experimental results indicated that our method is faster than existing alignment-based and alignment-free methods in E. coli and S. cerevisiae datasets. For large genome datasets, our method is slower than alignment-based methods but still faster than existing alignment-free method

Using Document Structure in Matching of Projected Slides

[[abstract]]This thesis proposes an image matching algorithm for matching up video document images against original documents. Our approach extracts the text and picture regions from the document, then build structure description to match up the original document without reconstructing the background. This makes original documents be manufactured with different background. The algorithm consists of four steps. First, the document content is segmented from the video and calibrated to the video frame size. We named the document content video document. Second, the video document is processed by document analysis, then the text and picture regions are extracted. Third, the text and picture region are used to build structure description individually. Finally, we calculate the confident value between the video document and each original document, and take the one which have the highest confident value to be the matching result. Experiments were conducted using fifty-one sets of slides and video files, and the number of all slides is 1153. We use the proposed algorithm to match up the video frame against the corresponding slide. The experimental results attain 97.4% precision rate in total slides. This shows the algorithm can be applied to the low quality video and slides with composite contents.

[[alternative]]Wavelet-based reversible and visible image watermarking scheme

碩士[[abstract]]本論文提出一個以小波轉換為基礎，輔以改良對比度之可逆浮水印技術。此嵌入技術可將浮水印影像嵌入至數位影像中，而僅需使用嵌入浮水印後的掩護影像及嵌入過程所產生的例外紀錄，即可還原獲得原數位影像，並獲得已嵌入的浮水印影像。 本論文之嵌入浮水印演算法步驟如下。首先將欲嵌入浮水印之影像以鄰近的四個像素為一組分割成四張相似的影像。接下來對分割後的影像分別使用小波轉換處理。然後將用小波轉換處理過後的影像取LL的區塊使用本論文所提之改良式對比強化浮水印嵌入技術嵌入浮水印。之後使用反小波轉換將嵌入浮水印後的小波影像轉換回影像。最後只要將轉換回來的影像合併，即可得到嵌入浮水印的影像。 安全性方面，本論文將原影像分割成四張相似的影像，取其中一張作為浮水印嵌入的參考，選取另外三張嵌入浮水印，提高了沒有本論文的方法時欲還原成原本影像的困難度，與其他論文比相對來說更加安全。[[abstract]]A visible reversible image watermarking scheme embeds visible watermark into digital images for ownership identification and the embedded watermark can be removed to recover the original image. The proposed scheme first partitions the cover image to four similar images with half size in width and height. These 4 images are classified to two sets, fixed set (FS) and watermarked set (WS), and each image is then applied to forward wavelet transform for acquiring 4 low-pass subimages for embedding watermark image. The number of FS and WS deter-mines the stability or visibility of embedded watermarks. The coefficients in FS are stationary and the watermark image is embedded into low-pass coefficients of WS based on low-pass coefficients of FS. Experimental results show that the proposed scheme has good watermark similarity and good extraction result under cropped or noise attacks.[[tableofcontents]]目錄 目錄 iii 圖目錄 v 表目錄 vi 第一章 緒論 1 1.1研究背景與動機 1 1.2浮水印相關研究 3 1.2.1可逆可見浮水印研究 4 1.3論文架構 5 第二章 相關研究 7 2.1小波轉換技術 7 2.2Lin [8]的方法 8 第三章 本論文提出方法 12 3.1嵌入流程 12 3.2影像還原與浮水印抽取流程 14 第四章 實驗結果及比較 16 4.1浮水印嵌入實驗 16 4.2與Lin[8]的方法作比較 20 4.3裁切影像還原實驗 22 4.4雜訊攻擊實驗 24 第五章 結論 28 參考文獻 29 附錄-研討會論文 31 圖目錄 圖1.1 Lena及小波轉換後的圖 8 圖4.1為本文用來嵌入掩護影像之黑白浮水印資訊 17 圖4.2以本文方法嵌入浮水印與還原後之結果比較圖 17 圖4.3以本文方法嵌入浮水印至不同掩護影像之結果比較圖 18 圖4.4分別以本文方法與Lin的方法嵌入浮水印之結果比較圖 21 圖4.5將以本文方法嵌入浮水印的兩張影像裁切後還原結果比較 23 圖4.6將掩護影像加入胡椒鹽雜訊後還原並抽取浮水印資訊的實驗結果 25 圖4.7將掩護影像加入高斯雜訊後還原並抽取浮水印資訊的實驗結果 27 表目錄 表4.1不同影像嵌入浮水印之溢位像素數及還原後之PSNR比較 19 表4.2不同裁切區塊與原圖wu值的比較 22[[note]]學號: 601420291, 學年度: 10

[[alternative]]Efficient face detection based on machine learning

碩士[[abstract]]機器學習是一個能解決許多問題且非常有用及有效的演算法，在這篇論文中利用兩種機器學習演算法分別去偵測膚色及人臉。首先，在膚色的偵測的部份，為了解決膚色易受光源的影響，分別針對膚色群聚的特性而取的特徵，來克服光源強弱的變化及解決近似膚色的問題，在找到膚色的區域後，並得到一膚色二值化的圖，利用形態學中斷開及閉合的運算消除雜訊，再利用長及寬的比例1:4過濾出可能的區塊。在這些區塊之中使用20 x 20的滑動視窗去偵測每一個區塊中是否有人臉的存在，進一步去判別是否為左臉，正臉或者是右臉，判別的依據正是使用Adaboost去挑出特徵。在特徵的選取上，是採用Haar-like特徵及我們選擇的變異數特徵以克服光線強弱對人臉所造成的影響。 實驗結果顯示，可以克服光線強弱對膚色造成的影響，及偵測人臉旋轉，及多人臉。[[abstract]]The machine learning is the state-of-the-art algorithm to solve all kinds of problems. This paper utilizes two types of machine learning algorithm to detect skin and face respectively. First, in the skin detection, to overcome the variance of light on the face is our most essential issue. According to the issue, two features chosen to serve as input of neural network dividedly, the first feature based on YCbCr to conquer the diversity of light, the second feature based on RGB to get over the color near the skin color and we get a binary map. Utilizing Opening and Closing to eliminate the noises and using the proportion of height and width to filter the candidate blocks. Second, in the face detection, the haar-like features[11][12] are utilized to serve as features of modified Adaboost to justify the left, frontal, right, or non-face in the 20 x 20 sliding window. Experimental results show that the proposed methods reach to better performance. In terms of skin color detection, capacity of coping with the problems of scaling, rotation and multiple faces, it results in good detection rate.[[tableofcontents]]目錄 第1章 緒論............................................................................................................1 1.1 研究動機與目的........................................................................................1 1.2 相關研究....................................................................................................1 第2章 相關理論....................................................................................................3 2.1 彩色模型.........................................................................................................4 2.1.1 RGB......................................................................................................4 2.1.2 YCbCr...................................................................................................5 2.1.3 HSV.......................................................................................................8 2.2 類神經網路-倒傳遞神經網路.....................................................................10 2.2.1 類神經網路的種類............................................................................11 2.2.2 類神經網路的介紹............................................................................12 2.3 AdaBoost........................................................................................................14 2.3.1 AdaBoost簡介.....................................................................................14 2.3.2 AdaBoost演算法.................................................................................15 第3章 研究方法..................................................................................................17 3.1 概要...............................................................................................................18 3.2 膚色的偵測...................................................................................................20 3.2.1 類神經網路參數設定........................................................................20 3.2.2 YCbCr特徵-第一階段........................................................................22 3.2.3 RGB特徵-第二階段...........................................................................23 3.3 AdaBoost的設計與運用................................................................................27 3.3.1 特徵選取............................................................................................27 3.3.2 弱分類器的建立................................................................................28 3.3.3 AdaBoost的訓練.................................................................................29 3.4 後處理...................................................................................................31 第4章 實驗結果與討論......................................................................................31 4.1膚色偵測系統評估........................................................................................31 4.1.1偵測錯誤的膚色.................................................................................32 4.2人臉偵測系統評估........................................................................................33 4.3 實驗結果.......................................................................................................36 第5章 結論與未來展望......................................................................................38 參考文獻......................................................................................................................38 圖 目 錄 圖2-1 RGB彩色模型..................................................................................................5 圖2-2 YCBCR顏色空間膚色分布圖........................................................................7 圖2-3 亮度在160時，膚色分布圖..........................................................................7 圖2-4 HSV顏色空間膚色分布圖及對應的顏色......................................................9 圖2-5 類神經示意圖................................................................................................10 圖2-6 倒傳遞類神經示意圖....................................................................................12 圖2-7 倒傳遞網路的網路架構圖............................................................................13 圖2-8 SIGMOID 函數式意圖....................................................................................13 圖3-1 系統流程圖....................................................................................................19 圖3-2 YCBCR特徵圖..............................................................................................23 圖3-3 膚色受外來因素影響的偵測結果................................................................24 圖3-4 膚色受光線強弱影響的偵測結果................................................................24 圖3-5 各種人種膚色的偵測結果............................................................................24 圖3-6 黑色人種膚色的偵測結果............................................................................25 圖3-7 複雜背景的偵測結果....................................................................................25 圖3-8 HAAR-LIKE特徵.............................................................................................28 圖3-9 弱分類器符號因子及門檻值........................................................................29 圖3-10 特徵的選取..................................................................................................31 圖4-1 膚色偵測結果................................................................................................32 圖4-2 膚色偵測結果................................................................................................33 圖4-3 訓練用的部分人臉及非人臉樣本................................................................34 圖4-4 修正後的正例訓練樣本................................................................................34 圖4-5 單純背景偵測結果........................................................................................36 圖4-6 複雜背景偵測結果........................................................................................37 圖4-7 左轉及右轉臉偵測結果................................................................................37 圖4-8 正臉偵測結果................................................................................................37 圖4-9 多人臉及複雜背景偵測結果........................................................................37 表 目 錄 表4-1 DETECTION RATE AND FALSE ALARM RATE比較...........................................35 表4-2 DETECTION RATE AND FALSE ALARM RATE比較...........................................36 公式 目 錄 式2-1 RGB膚色偵測門檻值......................................................................................5 式2-2 YCBCR膚色偵測門檻值................................................................................6 式2-3 YCBCR膚色偵測門檻值................................................................................8 式2-4 RGB轉換成HSV的公式..................................................................................9 式2-5 HSV膚色偵測門檻值....................................................................................10 式4-1 DETECTION RATE.............................................................................................34 式4-2 FALSE ALARM RATE.........................................................................................34[[note]]學號: 693191123, 學年度: 9

准线性光纤传输信道内四波混频及其抑制探究

对于高码率、长距离、色散管理准线性光纤传输,IFWM（信道内四波混频）是影响信号传输质量的主要非线性效应之一。文章采用分布傅里叶的计算方法,推导了描述准线性传输系统传输过程的数值计算模型,介绍了一种经济有效的、色散管理结合脉冲预展宽的非线性补偿方案。经过预展宽后,输出端鬼脉冲的强度仅为预展宽前的1/4。计算结果表明,该方案可有效抑制IFWM,获得理想的补偿效果,对今后高速光纤通信系统的设计具有一定的指导意义