收文單位：左列各單位 發文字號： MT-8-2-0037
上傳時間： 2013-10-28
上傳用戶：ming529
This application note provides users with a general understanding of the SVF and XSVF fileformats as they apply to Xilinx devices. Some familiarity with IEEE STD 1149.1 (JTAG) isassumed. For information on using Serial Vector Format (SVF) and Xilinx Serial Vector Format(XSVF) files in embedded programming applications
上傳時間： 2015-01-02
上傳用戶：時代將軍
This application note shows how to achieve low-cost, efficient serial configuration for Spartan FPGA designs. The approachrecommended here takes advantage of unused resources in a design, thereby reducing the cost, part count, memory size,and board space associated with the serial configuration circuitry. As a result, neither processor nor PROM needs to be fullydedicated to performing Spartan configuration.In particular, information is provided on how the idle processing time of an on-board controller can be used to loadconfiguration data from an off-board source. As a result, it is possible to upgrade a Spartan design in the field by sending thebitstream over a network.
上傳時間： 2013-11-01
上傳用戶：wojiaohs
MP3 portable players are the trend in music-listening technology. These players do not includeany mechanical movements, thereby making them ideal for listening to music during any type ofactivity. MP3 is a digital compression technique based on MPEG Layer 3 which stores music ina lot less space than current CD technology. Software is readily available to create MP3 filesfrom an existing CD, and the user can then download these files into a portable MP3 player tobe enjoyed in almost any environment.
上傳時間： 2013-11-23
上傳用戶：nanxia
enter——選取或啟動 esc——放棄或取消 f1——啟動在線幫助窗口 tab——啟動浮動圖件的屬性窗口 pgup——放大窗口顯示比例 pgdn——縮小窗口顯示比例 end——刷新屏幕 del——刪除點取的元件（1個） ctrl+del——刪除選取的元件（2個或2個以上） x+a——取消所有被選取圖件的選取狀態 x——將浮動圖件左右翻轉 y——將浮動圖件上下翻轉 space——將浮動圖件旋轉90度 crtl+ins——將選取圖件復制到編輯區里 shift+ins——將剪貼板里的圖件貼到編輯區里 shift+del——將選取圖件剪切放入剪貼板里 alt+backspace——恢復前一次的操作 ctrl+backspace——取消前一次的恢復 crtl+g——跳轉到指定的位置 crtl+f——尋找指定的文字
上傳時間： 2013-11-01
上傳用戶：a296386173
為了提高直接轉矩控制（DTC）系統定子磁鏈估計精度，降低電流、電壓測量的隨機誤差，提出了一種基于擴展卡爾曼濾波（EKF）實現異步電機轉子位置和速度估計的方法。擴展卡爾曼濾波器是建立在基于旋轉坐標系下由定子電流、電壓、轉子轉速和其它電機參量所構成的電機模型上，將定子電流、定子磁鏈、轉速和轉子角位置作為狀態變量，定子電壓為輸入變量，定子電流為輸出變量，通過對磁鏈和轉速的閉環控制提高定子磁鏈的估計精度，實現了異步電機的無速度傳感器直接轉矩控制策略，仿真結果驗證了該方法的可行性,提高了直接轉矩的控制性能。 Abstract: In order to improve the Direct Torque Control（DTC） system of stator flux estimation accuracy and reduce the current， voltage measurement of random error， a novel method to estimate the speed and rotor position of asynchronous motor based on extended Kalman filter was introduced. EKF was based on d-p axis motor and other motor parameters （state vector: stator current， stator flux linkage， rotor angular speed and position； input: stator voltage； output: staror current). EKF was designed for stator flux and rotor speed estimation in close-loop control. It can improve the estimated accuracy of stator flux. It is possible to estimate the speed and rotor position and implement asynchronous motor drives without position and speed sensors. The simulation results show it is efficient and improves the control performance.
上傳時間： 2015-01-02
上傳用戶：qingdou
隨著USB技術日趨成熟，USB開發者只需要關注頂層開發即可，這樣雖然減少了工作量，但容易使開發者忽略USB基礎理論與概念，導致的弊端在于開發者一旦遇到問題，往往不知如何解決。作者基于多年USB開發經驗，針對當前很多USB開發者容易混淆的概念，進行深入淺出的剖析，針對枚舉和重枚舉的區別、不同啟動方式的區別等問題，進行了歸納總結。本文從對比的角度分析問題，有助于開發者理清USB的工作機理。
標簽： USB
上傳時間： 2013-10-26
上傳用戶：zaocan888
C++作業，實現vector
標簽：
上傳時間： 2015-01-21
上傳用戶：亞亞娟娟123
Rainbow is a C program that performs document classification usingone of several different methods, including naive Bayes, TFIDF/Rocchio,K-nearest neighbor, Maximum Entropy, Support Vector Machines, Fuhr sProbabilitistic Indexing, and a simple-minded form a shrinkage withnaive Bayes.
標簽： classification different document performs
上傳時間： 2015-03-03
上傳用戶：希醬大魔王
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.
上傳時間： 2013-12-16
上傳用戶：亞亞娟娟123
