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Description: This getting started exercise will guide you through the step-by-step process of transforming a MATLAB floating-point model into a hardware module that can be implemented in silicon (FPGA or ASIC). The design is a general purpose FIR filter taken from the AccelDSP Examples directory.-This exercise will guide y ou through the step-by-step process of transfo rming a MATLAB floating-point model into a hard ware module that can be implemented in silicon ( FPGA or ASIC). The design is a general purpose FI R filter taken from the AccelDSP Examples direc tory.
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Size: 5489 |
Author: 杨平 |
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Description: The goal of this simple exercise is to look closely at some RF propagation models, and their implementation in ns2.
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Size: 3182 |
Author: 何星 |
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Description: This getting started exercise will guide you through the step-by-step process of transforming a MATLAB floating-point model into a hardware module that can be implemented in silicon (FPGA or ASIC). The design is a general purpose FIR filter taken from the AccelDSP Examples directory.-This exercise will guide y ou through the step-by-step process of transfo rming a MATLAB floating-point model into a hard ware module that can be implemented in silicon ( FPGA or ASIC). The design is a general purpose FI R filter taken from the AccelDSP Examples direc tory.
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Size: 5120 |
Author: 杨平 |
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Description: The goal of this simple exercise is to look closely at some RF propagation models, and their implementation in ns2. -The goal of this simple exercise is to look closely at some RF propagation models, and their implementation in ns2.
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Size: 3072 |
Author: 何星 |
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Description: 《密码学与网络安全》 第四版 的习题解答，英文版-Cryptography and Network Security to answer the fourth edition of the exercise
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Size: 838656 |
Author: ucvvf |
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Description: 程序主要包括：1．MyInit.m 数据初始化
输出参数：
XTrueZ 目标真实轨迹
Z0 目标观测值
T 采样周期
Q 系统误差阵
DeltaR 距离误差
DeltaSita 方位角误差
DeltaBeta 俯仰角误差
totaltime 运动时间
montimes 蒙特卡罗仿真次数
2． EKF.m
[Z1]= EKF
Z1: 输出EKF滤波后结果
3. CMKF.m
[Z2]= CMKF
Z2: 输出CMKF滤波后结果
4. GetR.m CMKF算法中误差阵R的计算
5． CompareEandC.m
EKF和CMKF、最小二乘滤波后结果比较
部分绘图代码不全，具体结果请参见 信号实验作业.doc 文档
6．source.m
产生带野值的数据
7. RemoveWilddata.m
野值剔除算法
-Procedures will include: 1. MyInit.m initialization data output parameters: XTrueZ target the real target trajectory Z0 sampling period T observed value Q system error array DeltaR distance error DeltaSita azimuth error DeltaBeta pitch angle error totaltime exercise time montimes the number of Monte-Carlo simulation 2. EKF.m [Z1] = EKF Z1: Output EKF filtered results 3. CMKF.m [Z2] = CMKF Z2: Output CMKF filtered results 4. GetR.m CMKF algorithm for the calculation of error R array 5. CompareEandC.m EKF and CMKF, least-squares filtering Comparison of part of the drawing code after the failure, specifically the results of experimental work, see the signal. Doc document 6. source.m generate the data with outliers 7. RemoveWilddata.m outliers removed Algorithm
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Size: 10240 |
Author: yanghaiyan |
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Description: 这是一个用C#写的翻转棋游戏,学习C#和.NET编程而做的一个练习 -This is a flip with a C# written in chess game to learn C# and. NET programming while doing an exercise
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Size: 268288 |
Author: imh5362 |
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Description: 运用C语言知识对做图功能的一次练习。运用做图函数来描绘心中的家园。-using C make a graphics
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Size: 1024 |
Author: pp |
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Description:
Simple exercise that calculate the Taylor expansion of the exponential
function.
Input variables: degree N
vector of evaluation points, x
At each step plots the Taylor polynomial and compare with the real
function
function y=taylor_exp(N,x)
printf("Order of the expansion: d ", N)
size(x)
y=ones(size(x))
plot(x,y,"r-",x,exp(x),"b-")
legend("n=0,exp(x)")
for n=1:N
y+=(1/factorial(n))*(x.^n)
plot(x,y,"r-",x,exp(x),"b-")
xlabel("x")
ylabel("f(x)")
legend("approx","exp(x)")
pause
end
endfunction
-
Simple exercise that calculate the Taylor expansion of the exponential
function.
Input variables: degree N
vector of evaluation points, x
At each step plots the Taylor polynomial and compare with the real
function
function y=taylor_exp(N,x)
printf("Order of the expansion: d ", N)
size(x)
y=ones(size(x))
plot(x,y,"r-",x,exp(x),"b-")
legend("n=0,exp(x)")
for n=1:N
y+=(1/factorial(n))*(x.^n)
plot(x,y,"r-",x,exp(x),"b-")
xlabel("x")
ylabel("f(x)")
legend("approx","exp(x)")
pause
end
endfunction
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Size: 4096 |
Author: ali |
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Description:
Simple exercise that calculate the Taylor expansion of the exponential
function.
Input variables: degree N
vector of evaluation points, x
At each step plots the Taylor polynomial and compare with the real
function
function y=taylor_exp(N,x)
printf("Order of the expansion: d ", N)
size(x)
y=ones(size(x))
plot(x,y,"r-",x,exp(x),"b-")
legend("n=0,exp(x)")
for n=1:N
y+=(1/factorial(n))*(x.^n)
plot(x,y,"r-",x,exp(x),"b-")
xlabel("x")
ylabel("f(x)")
legend("approx","exp(x)")
pause
end
endfunction
-
Simple exercise that calculate the Taylor expansion of the exponential
function.
Input variables: degree N
vector of evaluation points, x
At each step plots the Taylor polynomial and compare with the real
function
function y=taylor_exp(N,x)
printf("Order of the expansion: d ", N)
size(x)
y=ones(size(x))
plot(x,y,"r-",x,exp(x),"b-")
legend("n=0,exp(x)")
for n=1:N
y+=(1/factorial(n))*(x.^n)
plot(x,y,"r-",x,exp(x),"b-")
xlabel("x")
ylabel("f(x)")
legend("approx","exp(x)")
pause
end
endfunction
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Size: 9216 |
Author: ali |
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Description: 经典C语言学习书籍K&R的第二册的课后习题答案，CHM版本，方便拷贝编译，学习C语言的必须资料-Homework answer the second volume of the classic C language learning books, K & R, CHM version, convenience copying compiled learning C language must! !
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Size: 379904 |
Author: Safi |
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Description: 劳伦斯教授（罗格斯大学和加州大学圣巴巴拉分校），罗纳德·谢弗教授（斯坦福大学）的团队已经设计了的关于语音信号倒频谱分析的程序，适用，有创新点，里面有很好地注释资料。-The set of speech processing exercises have been designed by a team consisting of Prof. Lawrence Rabiner (Rutgers University and University of California, Santa Barbara), Prof. Ronald Schafer (Stanford University), Kirty Vedula and Siva Yedithi (Rutgers University). This exercise is intended to supplement the teaching material in the textbook “Theory and Applications of Digital Speech Processing” by L R Rabiner and R W Schafer.
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Size: 1135616 |
Author: xie zhijie |
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Description: SSD1 exercise 8.
osworkflow open source workflow e
java latest most classic 100 case
Java basic documents,16 chaps ,pd
google protocol buffer 2.5.0 java
The Mashi Bing JAVA teaching vide
Beida Jade Bird project, based on
the hibernate+Struts rental websi
Do not use JNI, through the JNAIV
\ R \ n \ r \ nPageOffice filled
java interview book, a major soft
Java myfaces source code includes
Very good the ssh interview and w
Mybatis automatic generation of d
Swing help documentation
Recently a lot of children s
JAVA integration of the PageOffic
Using java to achieve a non-symme
the java j2ee Struts2 tutorial. V
java polymorphism codes-SSD1 exercise 8.
osworkflow open source workflow e
java latest most classic 100 case
Java basic documents,16 chaps ,pd
google protocol buffer 2.5.0 java
The Mashi Bing JAVA teaching vide
Beida Jade Bird project, based on
the hibernate+Struts rental websi
Do not use JNI, through the JNAIV
\ R \ n \ r \ nPageOffice filled
java interview book, a major soft
Java myfaces source code includes
Very good the ssh interview and w
Mybatis automatic generation of d
Swing help documentation
Recently a lot of children s
JAVA integration of the PageOffic
Using java to achieve a non-symme
the java j2ee Struts2 tutorial. V
java polymorphism codes
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Size: 7168 |
Author: afdfzvb |
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Description: 本代码在MATLAB中采用均方误差和测度两种评价参数对维纳滤波和L-R算法复原的加入噪声的运动模型和高斯模型的图像复原效果进行了比较-The code in MATLAB using the mean square error and two evaluation parameters measure the effect of exercise on the model image restoration Wiener filtering and restoration LR algorithm and Gaussian noise is added to the model were compared
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Size: 3072 |
Author: liuhao |
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Description: R语言数据处理实战演练，提高大数据时代数据处理能力-R language data processing practical exercise, the era of big data to improve data processing capability
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Size: 15314944 |
Author: DHJ |
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Description: 创建了一个L样本用户指定窗口的窗口类型(包括矩形、三角形、汉明、修改汉明,损害,和布莱克曼)。靠窗重叠相加版本的转移(在步骤R样本)行使决定overlap-added windows满足采样性质。-This exercise creates an L sample window of a user-specified window type (including rectangular, triangular, Hamming,modified Hamming, Hann, and Blackman). By overlap-adding shifted versions of the window (in steps of R samples) the exercise determines whether the overlap-added windows satisfy the windows sampling property.
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Size: 1484800 |
Author: 付晓强 |
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Description: 混合R语言，pdf+word说明，一种经典的调用R语言的方法，有PDF说明，但pdf是英语版的，很好同时还可以锻炼英语水平(Mixed R language, pdf+word description, a classic call R language method, there are PDF instructions, but PDF is the English version, very good, at the same time can also exercise English level)
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Size: 141312 |
Author: 搓搓
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