The STWD100 watchdog timer circuits are self-contained devices which prevent systemfailures that are caused by certain types of hardware errors (non-responding peripherals,bus contention, etc.) or software errors (bad code jump, code stuck in loop, etc.).The STWD100 watchdog timer has an input, WDI, and an output, WDO (see Figure 2). Theinput is used to clear the internal watchdog timer periodically within the specified timeoutperiod, twd (see Section 3: Watchdog timing). While the system is operating correctly, itperiodically toggles the watchdog input, WDI. If the system fails, the watchdog timer is notreset, a system alert is generated and the watchdog output, WDO, is asserted (seeSection 3: Watchdog timing).The STWD100 circuit also has an enable pin, EN (see Figure 2), which can enable ordisable the watchdog functionality. The EN pin is connected to the internal pull-downresistor. The device is enabled if the EN pin is left floating.
上传时间： 2013-10-22
上传用户：taiyang250072
PCB 被动组件的隐藏特性解析 传统上，EMC一直被视为「黑色魔术（black magic）」。其实，EMC是可以藉由数学公式来理解的。不过，纵使有数学分析方法可以利用，但那些数学方程式对实际的EMC电路设计而言，仍然太过复杂了。幸运的是，在大多数的实务工作中，工程师并不需要完全理解那些复杂的数学公式和存在于EMC规范中的学理依据，只要藉由简单的数学模型，就能够明白要如何达到EMC的要求。本文藉由简单的数学公式和电磁理论，来说明在印刷电路板（PCB）上被动组件（passivecomponent）的隐藏行为和特性，这些都是工程师想让所设计的电子产品通过EMC标准时，事先所必须具备的基本知识。导线和PCB走线导线（wire）、走线（trace）、固定架……等看似不起眼的组件，却经常成为射频能量的最佳发射器（亦即，EMI的来源）。每一种组件都具有电感，这包含硅芯片的焊线（bond wire）、以及电阻、电容、电感的接脚。每根导线或走线都包含有隐藏的寄生电容和电感。这些寄生性组件会影响导线的阻抗大小，而且对频率很敏感。依据LC 的值（决定自共振频率）和PCB走线的长度，在某组件和PCB走线之间，可以产生自共振（self-resonance），因此，形成一根有效率的辐射天线。在低频时，导线大致上只具有电阻的特性。但在高频时，导线就具有电感的特性。因为变成高频后，会造成阻抗大小的变化，进而改变导线或PCB 走线与接地之间的EMC 设计，这时必需使用接地面（ground plane）和接地网格（ground grid）。导线和PCB 走线的最主要差别只在于，导线是圆形的，走线是长方形的。导线或走线的阻抗包含电阻R和感抗XL = 2πfL，在高频时，此阻抗定义为Z = R + j XL j2πfL，没有容抗Xc = 1/2πfC存在。频率高于100 kHz以上时，感抗大于电阻，此时导线或走线不再是低电阻的连接线，而是电感。一般而言，在音频以上工作的导线或走线应该视为电感，不能再看成电阻，而且可以是射频天线。
上传时间： 2013-11-16
上传用户：极客
国外游戏开发者杂志2003年第二期配套代码，包含了Jon Blow的交互工具的版本更新，使用了一个Kohonen Self-Organizing Feature Map来区分系统的行为
上传时间： 2014-01-19
上传用户：拔丝土豆
最新的支持向量机工具箱，有了它会很方便 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.
上传时间： 2013-12-16
上传用户：亚亚娟娟123
Abbrevia is a compression toolkit for Borland Delphi, C++Builder, & Kylix. It supports PKZIP 4, Microsoft CAB, TAR, & gzip formats & the creation of self-extracting archives. It includes visual components that simplify the manipulation of ZIP files.
标签： compression Abbrevia supports Borland
上传时间： 2014-01-13
上传用户：来茴
LVQ学习矢量化算法源程序 This directory contains code implementing the Learning vector quantization network. Source code may be found in LVQ.CPP. Sample training data is found in LVQ1.PAT. Sample test data is found in LVQTEST1.TST and LVQTEST2.TST. The LVQ program accepts input consisting of vectors and calculates the LVQ network weights. If a test set is specified, the winning neuron (class) for each neuron is identified and the Euclidean distance between the pattern and each neuron is reported. Output is directed to the screen.
标签： implementing quantization directory Learning
上传时间： 2015-05-02
上传用户：hewenzhi
This program demonstrates some function approximation capabilities of a Radial Basis Function Network. The user supplies a set of training points which represent some "sample" points for some arbitrary curve. Next, the user specifies the number of equally spaced gaussian centers and the variance for the network. Using the training samples, the weights multiplying each of the gaussian basis functions arecalculated using the pseudo-inverse (yielding the minimum least-squares solution). The resulting network is then used to approximate the function between the given "sample" points.
标签： approximation demonstrates capabilities Function
上传时间： 2014-01-01
上传用户：zjf3110
摘 : 通过使用 peer-to-peer(P2P)计算模式在 Internet 物理拓扑基础上建立一个称为 P2P 覆盖网络(P overlay network)的虚拟拓扑结构,有效地建立起一个基于 Internet 的完全分布式自组织网络路由模型 集中式自组织网络路由模型(hierarchical aggregation self-organizing network,简称 HASN).分别描述了 HASN 由模型的构建目标和体系结构,并详细分析了 HASN 采用的基于 P2P 计算模式的分布式命名 路由发现和更 算法 HASN_Scale,并在仿真实验的基础上,对 HASN 路由模型的性能进行了验证.
标签： peer-to-peer P2P Internet overlay
上传时间： 2014-01-21
上传用户：zhenyushaw
Blind Equalizer 的演算法主要是利用CMA及 LMS 的配合，当CMA将EYE打开，使讯号趋近于正确值，就切换到LMS，利用Slicer的输出当作training sequence来调整Equalizer的系数，而Carrier Recovery 的部份，则是将phase error track出来
上传时间： 2013-12-28
上传用户：it男一枚
C＋＋编写的机器学习算法 Lemga is a C++ package which consists of classes for several learning models and generic algorithms for optimizing (training) the models
标签： consists learning classes package
上传时间： 2014-01-21
上传用户：wangchong
