Contains watermark embedding
標(biāo)簽： watermark embedding Contains
上傳時(shí)間： 2014-12-21
上傳用戶：獨(dú)孤求源
Steganography is the art of communicating a message by embedding it into multimedia data. It is desired to maximize the amount of hidden information (embedding rate) while preserving security against detection by unauthorized parties. An appropriate information-theoretic model for steganography has been proposed by Cachin
標(biāo)簽： Steganography communicating multimedia embedding
上傳時(shí)間： 2017-07-29
上傳用戶：
lsb embedding for invisible image watermarking
標(biāo)簽： watermarking embedding invisible image
上傳時(shí)間： 2013-12-13
上傳用戶：xyipie
歡迎使用 PowerPCB 教程。本教程描述了 PADS-PowerPCB 的絕大部分功能和特點(diǎn)，以及使用的各個(gè)過程，這些功能包括： · 基本操作 · 建立元件(Component) · 建立板子邊框線(Board outline) · 輸入網(wǎng)表(Netlist) · 設(shè)置設(shè)計(jì)規(guī)則(Design Rule) · 元件(Part)的布局(Placement) · 手工和交互的布線 · SPECCTRA全自動(dòng)布線器(Route Engine) · 覆銅(Copper Pour) · 建立分隔/混合平面層(Split/mixed Plane) · Microsoft的目標(biāo)連接與嵌入(OLE)(Object Linking embedding) · 可選擇的裝配選件(Assembly options) · 設(shè)計(jì)規(guī)則檢查(Design Rule Check) · 反向標(biāo)注(Back Annotation) · 繪圖輸出(Plot Output) 使用本教程后，你可以學(xué)到印制電路板設(shè)計(jì)和制造的許多基本知識(shí)。
上傳時(shí)間： 2013-10-08
上傳用戶：x18010875091
C-C方法及改進(jìn)的C-C方法重構(gòu)相空間的matlab程序 -------------------------------- 性能： 3000數(shù)據(jù)耗時(shí)3分鐘 -------------------------------- 參考文獻(xiàn)： 1、Nonlinear dynamics, delay times, and embedding windows.pdf 2、基于改進(jìn)的C-C方法的相空間重構(gòu)參數(shù)選擇4.pdf -------------------------------- 文件夾說(shuō)明： 1、C_C_Method_luzhenbo2.m - 程序主文件，直接運(yùn)行此文件即可! 2、LorenzData.dll - 產(chǎn)生Lorenz離散數(shù)據(jù) 3、DuffingData.dll - 產(chǎn)生Duffing離散數(shù)據(jù) 4、RosslerData.dll - 產(chǎn)生Rossler離散數(shù)據(jù) 5、ccFunction.dll - 計(jì)算S(m,N,r,t) - 原C-C方法中計(jì)算S(m,N,r,t)，改進(jìn)的C-C方法中計(jì)算S2(m,N,r,t) 6、ccFunction_luzhenbo.dll - 計(jì)算S(m,N,r,t) - 改進(jìn)的C-C方法中計(jì)算S1(m,N,r,t) -------------------------------- 致謝： 此稿本次修改的部分靈感來(lái)源于與研學(xué)論壇網(wǎng)友“張文鴿”和“yangfanboy”的討論，在此表示感謝!
上傳時(shí)間： 2015-06-08
上傳用戶：lo25643
假近鄰法(False Nearest Neighbor, FNN)計(jì)算嵌入維的Matlab程序 文件夾說(shuō)明： Main_FNN.m - 程序主函數(shù)，直接運(yùn)行此文件即可 LorenzData.dll - 產(chǎn)生Lorenz時(shí)間序列 PhaSpaRecon.m - 相空間重構(gòu) fnn_luzhenbo.dll - 假近鄰計(jì)算主函數(shù) SearchNN.dll - 近鄰點(diǎn)搜索 buffer_SearchNN_1.dll - 近鄰點(diǎn)搜索緩存1 buffer_SearchNN_2.dll - 近鄰點(diǎn)搜索緩存2 參考文獻(xiàn)： M.B.Kennel, R.Brown, H.D.I.Abarbanel. Determining embedding dimension for phase-space reconstruction using a geometrical construction[J]. Phys. Rev. A 1992,45:3403.
標(biāo)簽： Main_FNN Neighbor Nearest Matlab
上傳時(shí)間： 2013-12-10
上傳用戶：songnanhua
This book will provide the necessary skills to create GUI, networking, and Web applications. It also will talk about extending and embedding Ruby applications
標(biāo)簽： applications networking necessary provide
上傳時(shí)間： 2014-01-14
上傳用戶：離殤
This document contains a general overview in the first few sections as well as a more detailed reference in later sections for SVMpython. If you re already familiar with SVMpython, it s possible to get a pretty good idea of how to use the package merely by browsing through svmstruct.py and multiclass.py. This document provides a more in depth view of how to use the package. Note that this is not a conversion of SVMstruct to Python. It is merely an embedding of Python in existing C code. All code other than the user implemented API functions is still in C, including optimization.
標(biāo)簽： document contains detailed overview
上傳時(shí)間： 2013-12-14
上傳用戶：希醬大魔王
We propose a novel approach for head tracking, which combines particle filters with Isomap. The particle filter works on the low-dimensional embedding of training images. It indexes into the Isomap with its state variables to find the closest template for each particle. The most weighted particle approximates the location of head. We develop a synthetic video sequence to test our technique. The results we get show that the tracker tracks the head which changes position, poses and lighting conditions.
標(biāo)簽： approach combines particle tracking
上傳時(shí)間： 2016-01-02
上傳用戶：yy541071797
這是LLE的原始算法，原文的參考文獻(xiàn)是：S.T.Roweis and L.K.Saul. Nonlinear dimensionality reduction by locally linear embedding. Science, 290, 2000.
上傳時(shí)間： 2013-12-20
上傳用戶：蟲蟲蟲蟲蟲蟲
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