用Java實(shí)現(xiàn)的一個(gè)簡(jiǎn)單的寄存器分配器，用的算法是線(xiàn)性?huà)呙?Linear Scan)
上傳時(shí)間： 2013-12-10
上傳用戶(hù)：zyt
外國(guó)人開(kāi)發(fā)的電磁時(shí)域有限差分方法工具包 Electromagnetic Finite-Difference Time-Domain (EmFDTD) is a basic two-dimensional FDTD code developed at the School of Electrical Engineering, Sharif University of Technology. This code has been written based on the standard Yee s FDTD algorithm. Applications include propagation, scattering, and diffraction of electromagnetic waves in homogeneous and non-homogeneous isotropic media for in-plane propagating waves. Negative permittivites or permeabilities as well as dispersion is not included. Zero, Periodic, and Perfectly Matched Layer boundary conditions may be selectively applied to the solution domain. The program is best suited for study of propagation and diffraction of electromagnetic waves in Photonic Crystal structures. EmFDTD is written in MATLAB language and has been tested under MATLAB 5.0 and higher versions.
標(biāo)簽： Finite-Difference Electromagnetic two-dimensio Time-Domain
上傳時(shí)間： 2014-11-24
上傳用戶(hù)：watch100
The inverse of the gradient function. I ve provided versions that work on 1-d vectors, or 2-d or 3-d arrays. In the 1-d case I offer 5 different methods, from cumtrapz, and an integrated cubic spline, plus several finite difference methods. In higher dimensions, only a finite difference/linear algebra solution is provided, but it is fully vectorized and fully sparse in its approach. In 2-d and 3-d, if the gradients are inconsistent, then a least squares solution is generated
標(biāo)簽： gradient function provided versions
上傳時(shí)間： 2016-11-07
上傳用戶(hù)：秦莞爾w
Boost provides free peer-reviewed portable C++ source libraries. We emphasize libraries that work well with the C++ Standard Library. Boost libraries are intended to be widely useful, and usable across a broad spectrum of applications. The Boost license encourages both commercial and non-commercial use. We aim to establish "existing practice" and provide reference implementations so that Boost libraries are suitable for eventual standardization. Ten Boost libraries are already included in the C++ Standards Committee s Library Technical Report (TR1) as a step toward becoming part of a future C++ Standard. More Boost libraries are proposed for the upcoming TR2. Boost works on almost any modern operating system, including UNIX and Windows variants. Follow the Getting Started Guide to download and install Boost. Popular Linux and Unix distributions such as Fedora, Debian, and NetBSD include pre-built Boost packages. Boost may also already be available on your organization s internal web server.
標(biāo)簽： libraries peer-reviewed emphasize provides
上傳時(shí)間： 2016-12-05
上傳用戶(hù)：semi1981
MATLAB Code for Optimal Quincunx Filter Bank Design Yi Chen July 17, 2006 This file introduces the MATLAB code that implements the two algorithms (i.e., Algorithms 1 and 2 in [1], or Algorithms 4.1 and 4.2 in [2]) used for the construction of quincunx filter banks with perfect reconstruction, linear phase, high coding gain, certain vanishing moments properties, and good frequency selectivity. The code can be used to design quincunx filter banks with two, three, or four lifting steps. The SeDuMi Matlab toolbox [3] is used to solve the second-order cone programming subproblems in the two algorithms, and must be installed in order for this code to work.
標(biāo)簽： introduces Quincunx Optimal MATLAB
上傳時(shí)間： 2014-01-15
上傳用戶(hù)：cc1
H.264/AVC, the result of the collaboration between the ISO/IEC Moving Picture Experts Group and the ITU-T Video Coding Experts Group, is the latest standard for video coding. The goals of this standardization effort were enhanced compression efficiency, network friendly video representation for interactive (video telephony) and non-interactive applications (broadcast, streaming, storage, video on demand). H.264/AVC provides gains in compression efficiency of up to 50％ over a wide range of bit rates and video resolutions compared to previous standards. Compared to previous standards, the decoder complexity is about four times that of MPEG-2 and two times that of MPEG-4 Visual Simple Profile. This paper provides an overview of the new tools, features and complexity of H.264/AVC.
標(biāo)簽： the collaboration between Experts
上傳時(shí)間： 2013-12-30
上傳用戶(hù)：dongbaobao
measure through the cross-entropy of test data. In addition, we introduce two novel smoothing techniques, one a variation of Jelinek-Mercer smoothing and one a very simple linear interpolation technique, both of which outperform existing methods.
標(biāo)簽： cross-entropy introduce smoothing addition
上傳時(shí)間： 2014-01-06
上傳用戶(hù)：qilin
Batch version of the back-propagation algorithm. ％ Given a set of corresponding input-output pairs and an initial network ％ [W1,W2,critvec,iter]=batbp(NetDef,W1,W2,PHI,Y,trparms) trains the ％ network with backpropagation. ％ ％ The activation functions must be either linear or tanh. The network ％ architecture is defined by the matrix NetDef consisting of two ％ rows. The first row specifies the hidden layer while the second ％ specifies the output layer. ％
標(biāo)簽： back-propagation corresponding input-output algorithm
上傳時(shí)間： 2016-12-27
上傳用戶(hù)：exxxds
％ Train a two layer neural network with the Levenberg-Marquardt ％ method. ％ ％ If desired, it is possible to use regularization by ％ weight decay. Also pruned (ie. not fully connected) networks can ％ be trained. ％ ％ Given a set of corresponding input-output pairs and an initial ％ network, ％ [W1,W2,critvec,iteration,lambda]=marq(NetDef,W1,W2,PHI,Y,trparms) ％ trains the network with the Levenberg-Marquardt method. ％ ％ The activation functions can be either linear or tanh. The ％ network architecture is defined by the matrix NetDef which ％ has two rows. The first row specifies the hidden layer and the ％ second row specifies the output layer.
標(biāo)簽： Levenberg-Marquardt desired network neural
上傳時(shí)間： 2016-12-27
上傳用戶(hù)：jcljkh
Train a two layer neural network with a recursive prediction error ％ algorithm ("recursive Gauss-Newton"). Also pruned (i.e., not fully ％ connected) networks can be trained. ％ ％ The activation functions can either be linear or tanh. The network ％ architecture is defined by the matrix NetDef , which has of two ％ rows. The first row specifies the hidden layer while the second ％ specifies the output layer.
標(biāo)簽： recursive prediction algorithm Gauss-Ne
上傳時(shí)間： 2016-12-27
上傳用戶(hù)：ljt101007
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