Inside the C++ Object Model Inside the C++ Object Model focuses on the underlying mechanisms that support object-oriented programming within C++: constructor semantics, temporary generation, support for encapsulation, inheritance, and "the virtuals"-virtual functions and virtual inheritance. This book shows how your understanding the underlying implementation models can help you code more efficiently and with greater confidence. Lippman dispells the misinformation and myths about the overhead and complexity associated with C++, while pointing out areas in which costs and trade offs, sometimes hidden, do exist. He then explains how the various implementation models arose, points out areas in which they are likely to evolve, and why they are what they are. He covers the semantic implications of the C++ object model and how that model affects your programs.
上傳時間： 2013-12-24
上傳用戶：zhouli
There are several problems related to the properties of the triangular mesh representation that describes a surface of an object. Sometimes, the surface is represented just as a set of triangles without any other information and the STL file format, which is used for data exchanges, is a typicalexampl e of this situation.
標簽： representation properties triangular the
上傳時間： 2014-11-12
上傳用戶：colinal
To get started you should be somewhat familiar with the components of the system. The soccer simulation consists of three important parts: the server, the monitor and the agents.
標簽： components the familiar somewhat
上傳時間： 2013-12-23
上傳用戶：gaome
MPEG-2 has 7 distinct parts as well. The first part is the Systems section which defines the container format and the Transport Streams that are designed to carry the digital video and audio over ATSC and DVB. The Program Stream defines the container format for lossy compression on optical disks, DVDs and SVCDs.
標簽： the distinct Systems defines
上傳時間： 2014-07-02
上傳用戶：奇奇奔奔
The literature of cryptography has a curious history. Secrecy, of course, has always played a central role, but until the First World War, important developments appeared in print in a more or less timely fashion and the field moved forward in much the same way as other specialized disciplines. As late as 1918, one of the most influential cryptanalytic papers of the twentieth century, William F. Friedman’s monograph The Index of Coincidence and Its Applications in Cryptography, appeared as a research report of the private Riverbank Laboratories [577]. And this, despite the fact that the work had been done as part of the war effort. In the same year Edward H. Hebern of Oakland, California filed the first patent for a rotor machine [710], the device destined to be a mainstay of military cryptography for nearly 50 years.
標簽： cryptography literature has Secrecy
上傳時間： 2016-12-08
上傳用戶：fxf126@126.com
The following is a list of MATLAB codes which includes the radar absorbing material design, the antenna pattern, the observation points generation, and the amplitude error and phase error calculations.
標簽： following absorbing the includes
上傳時間： 2014-01-04
上傳用戶：l254587896
This tutorial white-paper illustrates practical aspects of FIR filter design and fixed-point implementation along with the algorithms available in the Filter Design Toolbox and the Signal Processing Toolbox for this purpose.
標簽： illustrates fixed-point white-paper practical
上傳時間： 2016-12-14
上傳用戶：15071087253
This function calculates Akaike s final prediction error ％ estimate of the average generalization error. ％ ％ [FPE,deff,varest,H] = fpe(NetDef,W1,W2,PHI,Y,trparms) produces the ％ final prediction error estimate (fpe), the effective number of ％ weights in the network if the network has been trained with ％ weight decay, an estimate of the noise variance, and the Gauss-Newton ％ Hessian. ％
標簽： generalization calculates prediction function
上傳時間： 2014-12-03
上傳用戶：maizezhen
％ 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.
標簽： Levenberg-Marquardt desired network neural
上傳時間： 2016-12-27
上傳用戶：jcljkh
This function calculates Akaike s final prediction error ％ estimate of the average generalization error for network ％ models generated by NNARX, NNOE, NNARMAX1+2, or their recursive ％ counterparts. ％ ％ [FPE,deff,varest,H] = nnfpe(method,NetDef,W1,W2,U,Y,NN,trparms,skip,Chat) ％ produces the final prediction error estimate (fpe), the effective number ％ of weights in the network if it has been trained with weight decay, ％ an estimate of the noise variance, and the Gauss-Newton Hessian. ％
標簽： generalization calculates prediction function
上傳時間： 2016-12-27
上傳用戶：腳趾頭
