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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
標(biāo)簽：
Gauss-Seidel
iterative
following
methods
上傳時(shí)間：
2016-02-06
上傳用戶：zmy123
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The first task at hand is to set up the endpoints appropriately for this example. The following code switches the CPU clock speed
to 48 MHz (since at power-on default it is 12 MHz), and sets up EP2 as a Bulk OUT endpoint, 4x buffered of size 512, and EP6
as a Bulk IN endpoint, also 4x buffered of size 512. This set-up utilizes the maximum allotted 4-KB FIFO space. It also sets up
the FIFOs for manual mode, word-wide operation, and goes through a FIFO reset and arming sequence to ensure that they are
ready for data operations
標(biāo)簽：
appropriately
The
endpoints
following
上傳時(shí)間：
2013-12-02
上傳用戶：dianxin61
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//
// 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.
標(biāo)簽：
Sample
Histogram
Grabber
sample
上傳時(shí)間：
2013-12-15
上傳用戶：ryb
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In this demo, I use the EM algorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve recently derived, to train a two-layer perceptron, so as to classify medical data (kindly provided by Steve Roberts and Will Penny from EE, Imperial College). The data and simulations are described in: Nando de Freitas, Mahesan Niranjan and Andrew Gee Nonlinear State Space Estimation with Neural Networks and the EM algorithm After downloading the file, type "tar -xf EMdemo.tar" to uncompress it. This creates the directory EMdemo containing the required m files. Go to this directory, load matlab5 and type "EMtremor". The figures will then show you the simulation results, including ROC curves, likelihood plots, decision boundaries with error bars, etc. WARNING: Do make sure that you monitor the log-likelihood and check that it is increasing. Due to numerical errors, it might show glitches for some data sets.
標(biāo)簽：
Rauch-Tung-Striebel
algorithm
smoother
which
上傳時(shí)間：
2016-04-15
上傳用戶：zhenyushaw
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北京大學(xué)ACM題
Here is a geometric problem. You have an angle and some squares in the first quadrant of the plane rectangular coordinates. The vertex of the angle is fixed on the origin O of the coordinates, and both of its radial lines are specified by the input. The sizes of the squares are also specified by the input, and the squares can shift vertically and horizontally. Now your job is to use the squares and the radial lines of the angle to enclose the maximum area, which excludes the area of the squares (see Figure 1). You should note that the edges of the squares must be parallel to the axes.
標(biāo)簽：
geometric
quadrant
problem
squares
上傳時(shí)間：
2013-12-25
上傳用戶：ynzfm
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北京大學(xué)ACM比賽題目
Consider an infinite full binary search tree (see the figure below), the numbers in the nodes are 1, 2, 3, .... In a subtree whose root node is X, we can get the minimum number in this subtree by repeating going down the left node until the last level, and we can also find the maximum number by going down the right node. Now you are given some queries as "What are the minimum and maximum numbers in the subtree whose root node is X?" Please try to find answers for there queries.
標(biāo)簽：
the
Consider
infinite
numbers
上傳時(shí)間：
2013-12-16
上傳用戶：日光微瀾
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Obtain the CDF plots of PAPR from an OFDM BPSK transmission specified per IEEE 802.11a specification
Per the IEEE 802.11a specifications, we 52 have used subcarriers. Given so, the theoretical maximum expected PAPR is 52 (around 17dB). However, thanks to the scrambler, all the subcarriers in an OFDM symbol being equally modulated is unlikely.
Using a small script, the cumulative distribution of PAPR from each OFDM symbol, modulated by a random BPSK signal is obtained
標(biāo)簽：
specification
transmission
specified
Obtain
上傳時(shí)間：
2016-04-24
上傳用戶：haoxiyizhong
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This LDPC software is intended as an introduction to LDPC codes computer based simulation. The pseudo-random irregular low density parity check matrix is based on Radford M. Neal’s programs collection, which can be found in [1]. While Neal’s collection is well documented, in my opinion, C source codes are still overwhelming, especially if you are not knowledgeable in C language. My software is written for MATLAB, which is more readable than C. You may also want to refer to another MATLAB based LDPC source codes in [2], which has different flavor of code-writing style (in fact Arun has error in his log-likelihood decoder).
標(biāo)簽：
LDPC
introduction
simulation
software
上傳時(shí)間：
2014-01-14
上傳用戶：大融融rr
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Sequential Monte Carlo without Likelihoods
粒子濾波不用似然函數(shù)的情況下
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.
標(biāo)簽：
Likelihoods
Sequential
Bayesian
without
上傳時(shí)間：
2016-05-26
上傳用戶：離殤
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This LDPC software is intended as an introduction to LDPC codes computer based simulation. The pseudo-random irregular low density parity check matrix is based on Radford M. Neal’s programs collection, which can be found in [1]. While Neal’s collection is well documented, in my opinion, C source codes are still overwhelming, especially if you are not knowledgeable in C language. My software is written for MATLAB, which is more readable than C. You may also want to refer to another MATLAB based LDPC source codes in [2], which has different flavor of code-writing style (in fact Arun has error in his log-likelihood decoder).
標(biāo)簽：
LDPC
introduction
simulation
software
上傳時(shí)間：
2014-12-05
上傳用戶：change0329
