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  • Using Jacobi method and Gauss-Seidel iterative methods to solve the following system The require

    Using Jacobi method and Gauss-Seidel iterative methods to solve the following system The required precision is   =0.00001, and the maximum iteration number N=25. Compare the number of iterations and the convergence of these two methods

    标签: Gauss-Seidel iterative following methods

    上传时间: 2016-02-06

    上传用户:zmy123

  • μC/OS-II Goals Probably the most important goal of μC/OS-II was to make it backward compatible with

    μC/OS-II Goals Probably the most important goal of μC/OS-II was to make it backward compatible with μC/OS (at least from an application’s standpoint). A μC/OS port might need to be modified to work with μC/OS-II but at least, the application code should require only minor changes (if any). Also, because μC/OS-II is based on the same core as μC/OS, it is just as reliable. I added conditional compilation to allow you to further reduce the amount of RAM (i.e. data space) needed by μC/OS-II. This is especially useful when you have resource limited products. I also added the feature described in the previous section and cleaned up the code. Where the book is concerned, I wanted to clarify some of the concepts described in the first edition and provide additional explanations about how μC/OS-II works. I had numerous requests about doing a chapter on how to port μC/OS and thus, such a chapter has been included in this book for μC/OS-II.

    标签: OS-II compatible important Probably

    上传时间: 2013-12-02

    上传用户:jkhjkh1982

  • 这是PC机间相互通信的例子

    这是PC机间相互通信的例子,程序“require”为用查询方式实现,程序“interrupt”为 用中断方式实现

    标签: PC机 互通

    上传时间: 2013-12-29

    上传用户:tzl1975

  • 这是PC机间相互通信的例子

    这是PC机间相互通信的例子,程序“require”为用查询方式实现,程序“interrupt”为 用中断方式实现

    标签: PC机 互通

    上传时间: 2014-11-01

    上传用户:515414293

  • // // Histogram Sample // This sample shows how to use the Sample Grabber filter for video image p

    // // Histogram Sample // This sample shows how to use the Sample Grabber filter for video image processing. // Conceptual background: // A histogram is just a frequency count of every pixel value in the image. // There are various well-known mathematical operations that you can perform on an image // using histograms, to enhance the image, etc. // Histogram stretch (aka automatic gain control): // Stretches the image histogram to fill the entire range of values. This is a "point operation," // meaning each pixel is scaled to a new value, without examining the neighboring pixels. The // histogram stretch does not actually require you to calculate the full histogram. The scaling factor // is calculated from the minimum and maximum values in the image.

    标签: Sample Histogram Grabber sample

    上传时间: 2013-12-15

    上传用户:ryb

  • Matlab is an ideal tool for simulating digital communications systems, thanks to its easy scripting

    Matlab is an ideal tool for simulating digital communications systems, thanks to its easy scripting language and excellent data visualization capabilities. One of the most frequent simulation tasks in the field of digital communications is bit-error- rate testing of modems. The bit-error-rate performance of a receiver is a figure of merit that allows different designs to be compared in a fair manner. Performing bit-error-rate testing withMatlab is very simple, but does require some prerequisite knowledge

    标签: communications simulating scripting digital

    上传时间: 2014-01-02

    上传用户:plsee

  • SimpliciTI™ -1.0.3.exe for CC11xx and CC25xx SimpliciTI is a simple low-power RF network proto

    SimpliciTI™ -1.0.3.exe for CC11xx and CC25xx SimpliciTI is a simple low-power RF network protocol aimed at small (<256) RF networks. Such networks typically contain battery operated devices which require long battery life, low data rate and low duty cycle and have a limited number of nodes talking directly to each other or through an access point or range extenders. Access point and range extenders are not required but provide extra functionality such as store and forward messages. With SimpliciTI the MCU resource requirements are minimal which results in the low system cost.

    标签: SimpliciTI low-power network simple

    上传时间: 2014-11-05

    上传用户:rishian

  • SimpliciTI™ -1.0.4.exe for CC2430 SimpliciTI is a simple low-power RF network protocol aimed

    SimpliciTI™ -1.0.4.exe for CC2430 SimpliciTI is a simple low-power RF network protocol aimed at small (<256) RF networks. Such networks typically contain battery operated devices which require long battery life, low data rate and low duty cycle and have a limited number of nodes talking directly to each other or through an access point or range extenders. Access point and range extenders are not required but provide extra functionality such as store and forward messages. With SimpliciTI the MCU resource requirements are minimal which results in the low system cost.

    标签: SimpliciTI low-power protocol network

    上传时间: 2016-05-21

    上传用户:R50974

  • Sequential Monte Carlo without Likelihoods 粒子滤波不用似然函数的情况下 本文摘要:Recent new methods in Bayesian simu

    Sequential Monte Carlo without Likelihoods 粒子滤波不用似然函数的情况下 本文摘要:Recent new methods in Bayesian simulation have provided ways of evaluating posterior distributions in the presence of analytically or computationally intractable likelihood functions. Despite representing a substantial methodological advance, existing methods based on rejection sampling or Markov chain Monte Carlo can be highly inefficient, and accordingly require far more iterations than may be practical to implement. Here we propose a sequential Monte Carlo sampler that convincingly overcomes these inefficiencies. We demonstrate its implementation through an epidemiological study of the transmission rate of tuberculosis.

    标签: Likelihoods Sequential Bayesian without

    上传时间: 2016-05-26

    上传用户:离殇

  • The last step in training phase is refinement of the clusters found above. Although DynamicClusteri

    The last step in training phase is refinement of the clusters found above. Although DynamicClustering counters all the basic k-means disadvantages, setting the intra-cluster similarity r may require experimentation. Also, a cluster may have a lot in common with another, i.e., sequences assigned to it are as close to it as they are to another cluster. There may also be denser sub-clusters within the larger ones.

    标签: DynamicClusteri refinement Although clusters

    上传时间: 2014-01-04

    上传用户:watch100