This application note describes how to build a system that can be used for determining theoptimal phase shift for a Double Data Rate (DDR) memory feedback clock. In this system, theDDR memory is controlled by a controller that attaches to either the OPB or PLB and is used inan embedded microprocessor application. This reference system also uses a DCM that isconfigured so that the phase of its output clock can be changed while the system is running anda GPIO core that controls that phase shift. The GPIO output is controlled by a softwareapplication that can be run on a PowerPC® 405 or Microblaze™ microprocessor.
針對傳統集成電路（ASIC）功能固定、升級困難等缺點，利用FPGA實現了擴頻通信芯片STEL-2000A的核心功能。使用ISE提供的DDS IP核實現NCO模塊，在下變頻模塊調用了硬核乘法器并引入CIC濾波器進行低通濾波，給出了DQPSK解調的原理和實現方法，推導出一種簡便的引入?仔/4固定相移的實現方法。采用模塊化的設計方法使用VHDL語言編寫出源程序，在Virtex-II Pro 開發板上成功實現了整個系統。測試結果表明該系統正確實現了STEL-2000A的核心功能。
Abstract:
To overcome drawbacks of ASIC such as fixed functionality and upgrade difficulty, FPGA was used to realize the core functions of STEL-2000A. This paper used the DDS IP core provided by ISE to realize the NCO module, called hard core multiplier and implemented CIC filter in the down converter, described the principle and implementation detail of the demodulation of DQPSK, and derived a simple method to introduce a fixed phase shift of ?仔/4. The VHDL source code was designed by modularity method , and the complete system was successfully implemented on Virtex-II Pro development board. Test results indicate that this system successfully realize the core function of the STEL-2000A.
Complete support for EBNF notation; Object-oriented parser design; C++ output; Deterministic bottom-up "shift-reduce" parsing; SLR(1), LALR(1) and LR(1) table construction methods; Automatic parse tree creation; Possibility to output parse tree in XML format; Verbose conflict diagnostics; Generation of tree traverse procedures
This program will ask how many numbers you want to find the average of, then it will allow you to enter your numbers(yes they can even be decimals) then it will calculate the mean, median, mode and range of what you enter.
This a Bayesian ICA algorithm for the linear instantaneous mixing model with additive Gaussian noise [1]. The inference problem is solved by ML-II, i.e. the sources are found by integration over the source posterior and the noise covariance and mixing matrix are found by maximization of the marginal likelihood [1]. The sufficient statistics are estimated by either variational mean field theory with the linear response correction or by adaptive TAP mean field theory [2,3]. The mean field equations are solved by a belief propagation method [4] or sequential iteration. The computational complexity is N M^3, where N is the number of time samples and M the number of sources.
