This example provides a description of how to use the USART with hardware flowcontrol and communicate with the Hyperterminal.First, the USART2 sends the TxBuffer to the hyperterminal and still waiting fora string from the hyperterminal that you must enter which must end by '\r'character (keypad ENTER button). Each byte received is retransmitted to theHyperterminal. The string that you have entered is stored in the RxBuffer array. The receivebuffer have a RxBufferSize bytes as maximum.
The USART2 is configured as follow: - BaudRate = 115200 baud - Word Length = 8 Bits - One Stop Bit - No parity - Hardware flow control enabled (RTS and CTS signals) - Receive and transmit enabled - USART Clock disabled - USART CPOL: Clock is active low - USART CPHA: Data is captured on the second edge - USART LastBit: The clock pulse of the last data bit is not output to the SCLK pin
USB接口控制器參考設計,xilinx提供VHDL代碼 usb xilinx vhdl
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TheTool is highy customizable map editor(based on QT) which can be extended via LUA scripts. A first implentation is ready and can be published to the public. The editor is the perfect tool who wants to design some 3d games but don t have time to write
a Java program that reads a file containing instructions written in self-defined file (TPL in this case), and executes those instructions. This program should take the name of the TPL file as a command line parameter, and write its output to the console.
OFELI is an object oriented library of C++ classes for development of finite element codes. Its main features are : * Various storage schemes of matrices (dense, sparse, skyline). * Direct methods of solution of linear systems of equations as well as various combinations of iterative solvers and preconditioners. * Shape functions of most "popular" finite elements * Element arrays of most popular problems (Heat Transfer, Fluid Flow, Solid Mechanics, Electromagnetics, ...).
最新的支持向量機工具箱,有了它會很方便 1. Find time to write a proper list of things to do! 2. Documentation. 3. Support Vector Regression. 4. Automated model selection. REFERENCES ========== [1] V.N. Vapnik, "The Nature of Statistical Learning Theory", Springer-Verlag, New York, ISBN 0-387-94559-8, 1995. [2] J. C. Platt, "Fast training of support vector machines using sequential minimal optimization", in Advances in Kernel Methods - Support Vector Learning, (Eds) B. Scholkopf, C. Burges, and A. J. Smola, MIT Press, Cambridge, Massachusetts, chapter 12, pp 185-208, 1999. [3] T. Joachims, "Estimating the Generalization Performance of a SVM Efficiently", LS-8 Report 25, Universitat Dortmund, Fachbereich Informatik, 1999.