the scientist and engineer's guide to digital signal processing部分翻译

姓名柯林波班级07应用物理（2）班学号07207030203成绩

考试内容：2010年第一学期数字信号处理

第一部分解答题

1. Please give the stages of digital processing of analog signals and the basic

components of DSP system.

2. Please describe Sampling Theorem Compute the z-transform of the following

sequences x (n )

x (n ) = (-0.5)n u(n )

3. Try to test the linearity and time invariance of the discrete time systems

defined as follows:

)1()()(--=n x n x n y

4. Given a causal IIR discrete-time system described by the difference equation y[n]-0.4y[n-1]=x[n].

And it is known that the input sequence is x[n]= x[n]=(0.3)n μ[n]. .

(1)Determine the output sequence y[n] using the z-transform.

(2)Determine the expression of the frequency response H(e j ω) in the form |H(e j ω)|e j ?(ω)

第二部分文献翻译

参考文献：the scientist and engineer's guide to digital signal processing

具体内容：第285页第二段---第292页

原文：见附页

Part 1. 解答下列习题

1. Please give the stages of digital processing of analog signals and the basic

components of DSP system.

答：

(1)信号的数字化需要进过采样、保持、量化和编码四个过程

(2)数字信号系统的基本组成为：前置预滤波器、A/D 变换器、数字信号处理器、D/A 变换器、模拟滤波器。

2. Please describe Sampling TheoremCompute the z-transform of the following

sequences x (n )，x (n ) = (-0.5)n u(n )

解：(1)

采样定理：在进行模拟/数字信号的转换过程中，当采样频率大于信号中最高频率的2倍时，采样之后的数字信号完整地保留了原始信号中的信息，一般实际应用中保证采样频率为信号最高频率的5～10倍；采样定理又称奈奎斯特定理。

(2)

由题有：

()()()1010.51 0.510.5n n n n X z x n z z z z

∞-=-∞

∞-=-=

??=-??=>-+∑∑

3. Try to test the linearity and time invariance of the discretetime systems

defined as follows:)1()()(--=n x n x n y

解：

验证线性：

()()()

()()()()()()()

12012121211x n ax n bx n y n ax n bx n ax n bx n ay n by n →+=+----=+

验证时不变性：

()()

()()()

()

0 n m 1n n m x n x n m y n x n m x n m y n m +

→+∈→+=+-+-=+ 、

综上，此系统为线性时不变系统。

4.Given a causal IIR discrete-time system described by the difference equation y[n]-0.4y[n-1]=x[n].

And it is known that the input sequence is x[n]= x[n]=(0.3)n μ[n]. .

(1)Determine the output sequence y[n] using the z-transform.

(2)Determine the expression of the frequency response H(e j ω) in the form |H(e j ω)|e j ?(ω)

解：

(1)

先对元方程做z 变换，有：

()()()10.4Y z z Y z X z --=

其中：

()11 0.310.3X z z z

-=

>- 我们得到： ()11

11 0.410.310.4Y z z z z --=>-- 对其做逆z 变换，即得所求：

()()()()()

()10.30.43140.4 n 0314

n n n n y n n n μμ+=*??- ???=≥-

(2)

由上可知： ()11 0.410.4H z z z -=

>- 代换变量后有：

()()10.4cos 0.4sin 1.160.8cos j j H e ωωωω--=

-

其模为： ()11.160.8cos j H e ωω

=- 相位为：

()0.4sin arcsin 1.160.8cos ω?ωω-=-

以上即所求解答。

Part2 翻译

第十六章窗式正弦滤波器

窗式正弦滤波器是用来从一个频带中分离出特定频率波段工具。它们非常稳定、产生很少意外的偏差,而且还可推到令人难以置信的性能水平。这些独特的频域特性在时域中不能很好的表现出来（其中包括在阶跃响应中过度波纹和超调）。当通过标准卷积滤波器时窗式正弦滤波器易于编程,但执行起来缓慢。第18章演示了FFT 可以用来大大提高这些过滤器的计算速度。

窗式正弦设计思想

图16-1说明了窗式正弦滤波器背后的想法。如图（a ）所示，理想低通滤波器的频率响应。频率低于截止频率的信号，均能以原来的振幅通过，而所有的高于截止频率的均被封锁。通频带是完全通畅的，阻带衰减是无穷的，两者之间的过渡是无穷小的。以这种理想的频率响应的逆傅立叶变换产生理想滤波器内核（脉冲响应）如（b ）项。如前所述（参见第11章，式11-4），这条曲线的一般形式是：的sin （x ）/ x 内，称为sinc 函数，计算公式如下：

sin(2)[]c f i h i i ππ

= 此滤波器为卷积内核的输入信号提供了完善的低通滤波器。问题是，这种 Sinc 函数连续的从正无穷和负无穷，不会下降到零幅度。虽然这不是一个无限长的数学问题，但它是一个计算机的显示瓶颈。

为了解决这个问题，在（b ）图中我们对Sinc 函数将作出两项修改，结果如（c ）图中的波形。首先，我们将它在主瓣周围对称的截断到M+1点，，其中M 为偶数。这M+1点外面的所有样本点都设置为零，或者干脆被忽略。其次，整个序列右移，使其从0运行到M 。这样做是为了只用正指数取代滤波器内核。尽管许多编程语言允许负指数，但它们只是作为滋扰使用。这个M/2的滤波器内核转变的唯一效果是通过同样数额去平移输出信号。

由于修改后的滤波器的内核只是一个理想滤波器内核的近似，不会有一个理想的频率响应。为了得到通过滤波器信号的频率响应，在图（c ）中我们运用傅立叶变换来处理信号，得到的结果如图（d ）中的曲线。有个很糟糕的问题，在通带中有许多毛庛而在阻带中有过多的波信号在衰减（回忆吉布斯效应第11章中讨论）。这些问题来自于截断sinc 函数不连续而引起的。增加滤波器的内核的长度就不会出现这类问题了。无论M 被制成多长，不连续性任然是很显著的。

幸运的是，有一个简单的方法改善这种状况。图（e ）显示一个光滑圆锥曲线称为Blackman （布莱克）窗函数。将上面截断的sinc 函数与Blackman （布莱克）窗函数相乘，在窗式正弦滤波器内核结果显示如图（f ）所示。这样做是为了减少被截断的两端突起，从而提高频率响应。图（g ）显示这一改进。此时的通带是通畅的，阻带衰减是那么好，但不能在这个图中显示出来。

一些几种不同的窗口是可用的，其中大多数是在20世纪50年代后，以原来的开发商的名字命名。只有两个值得使用，汉明窗和布莱克曼窗，这些都是通过：

公式16-1

汉明窗。这些窗口上运行，从i=0到M，总共有M+1个点

方程16-2 Blackman（布莱克）窗口

[]0.540.46cos(2/)

w i M

π

=-

[]0.420.5cos(2/)0.08cos(4/) w i i M i M

ππ=-+

图16 - 2a显示了这两个窗口为M = 50（即总共51曲线点）的形状。这两个窗口，你应该使用哪一个？这个需要在参量之间平衡。如图所示，16 - 2b中，汉明窗的速度滚比布莱克窗有大约20％快。然而，图16-1（右）推导窗式SINC滤波器内核。理想低通滤波器的频率响应如（a），与（b）图sinc函数滤波器内核相对应，。由于Sinc是无限长，它必须被截断，以便在计算机上使用，如图（c）。然而，此截断在频率响应中会产生不良变化的结果，如图（d）。该解决方案是用一个光滑的窗口乘以截的sinc，（e），在窗式SINC滤波器内核产生结果，（f）。该窗式正弦频率响应，（克），是光滑的，也很规矩。这些数字达不到标准。

第16章- 窗式SINC

滤波器

图16-2是布莱克曼和汉明窗口特征。

这两个窗口的形状在（a）中显示，以

及由方程6-1和16-2给出。如图（b）

所示，汉明窗在滚降速度上Blackman

窗大约快20％。然而，Blackman窗具

有较好的阻带衰减（布莱克曼0.02％，

海明：0.2％），以及较低的通带纹波（布

莱克曼：0.02％海明：0.2％）。

由图（c）可以看出，布莱克曼具有较好的阻带衰减。为了更加精确，阻带衰减为布莱克曼是-74分贝（-0.02％），而只有海明-73分贝（-0.2％）。虽然不能在这些图可以看出，布莱克曼也只有约0.02％通带纹波，而海明通常0.2％。一般来

说，布莱克曼应该是您的第一选择;一个缓慢滚降是比低劣阻带衰减更容易处理。

你可能听说过还有一些其他的窗口，但它们比汉明和布莱克曼更短。巴特利特窗口的一个三角形，使用锥度直线。Hanning 窗，也叫升余弦窗，为：

[]0.50.5cos(2/)w i M π=-

这两个窗口和汉明有大约相同的滚降速度，但在阻带衰减更差（巴特利特：- 25dB 的5.6％，寒凝-四十四分贝或0.63％）。您可能还听到一个矩形窗口。这个没有窗户，只有一个截断的尾巴（如图16 - 1C 型）。虽然滚降是布莱克曼的-2.5倍，阻带衰减只有- 21dB 之间（8.9％）。

过滤器的设计

要设计一窗式sinc 函数，两个参数必须选择：截止频率fc ，以及滤波器的内核长度M

。

图16-3

滤波器长度主场对比窗式SINC 滤波器的滚降。如图所示（a ），为M = 20，40和200，过渡带宽采样率分别为BW= 0.2，0.1和0.02。如图（b ）所示，频率响应的形状在不同截止频率下不发生改变。在（b ）中，M= 60表示为一小部分的采样率，因此必须在0和0.5。根据M 近似值设置滚降：

方程16-3 滤波器长度与滚降 滤波器内核的长度，M ，确定过渡 带宽的滤波器的带宽，BW 。 这只是一个取决于被使用的特定窗口的近似正弦滚降：

4M BW

≈

其中BW 是过渡带宽，从那里测量仅仅留下一个曲线，几乎达到零（也就是说，99％至1％的曲线）。过渡带宽也表示为采样频率的一小部分，必须在0和0.5。图16 - 3a 显示了如何使用这种近似的例子。这三个曲线显示的是产生滤波器内核：M= 20，40和200。从方程16-3，过渡带宽为：BW= 0.2，0.1和0.02。图（b ）显示，该频率响应的形状不依赖于截止频率的选择。

因为对于一个卷积所需的时间正比的信号的长度，方程16-3表达了平衡计算

时间（依赖于M 的值而定）和滤波器的清晰度（即体重值）。例如，比Blackman 窗（与海明相比）慢20％的窗口，可以被弥补通过使用增长20％的滤波器内核。换句话说， 20％的慢执行Blackman 窗相当于一个滚降汉明窗。这一点很重要，因为窗式SINC 滤波器的执行速度已经非常缓慢。

又如图所示16 - 3B ，测量窗式SINC 滤波器的截止频率是在一个半幅度点。为什么要使用0.5而不是像在模拟电子技术和其他数字滤波器中使用（- 3dB ）的标准的0.707？这是因为窗式正弦的频率响应在通带和阻带之间对称的。例如，汉明窗中的结果是在0.2％的通带纹波，相同的阻带衰减（即纹波阻带的0.2％）。其他滤波器没有显示这种对称性，因此使用一个半幅度点在以表示截止频率上没有优势。正如本章后面所示，这种对称性使得窗式正弦在光谱反演上过于理想化。

在c f 和M 后已选定，滤波器的核心是从关系和计算之间计算：

sin(2(/2))24[][0.420.5cos()0.08cos()]/2c f i M i i h i k i M M M πππ-=-+-

方程16-4 窗式SINC 滤波器内核。截止频率，c f ，表示为一部分采样率，具体数值在0和0.5之间。滤波器内核的长度是由M 决定，必须是偶数。样本数i ，是一个从0到Mde 整数，总共有M +1导在滤波器的内核中。常数，K ，是选择提供单位增益频率为零。因为/2i M =，为了避免除以零的错误，使用[]2c h i f K π=。

不要被这个等式吓倒！在前面的讨论的基础上，你应该能够识别三部分组成：sinc 函数的M / 2的转变，以及Blackman 窗。滤波器的有一致的直流增益，常数K 必须这样选择，所有的样品和等于一。在实践中，在过滤器的内核计算中忽视K ，然后再正常化所有需要的样品。表16-1列出的程序显示了如何做到这一点。还要注意的是如何计算在处理正弦中心点，即i=M/ 2，其中涉及到被零除。

这个方程可能很长，但很容易使用，只需输入到您的计算机程序中，并忘记它。让计算机处理计算。如果你试图用手来评估这个公式，那么你正在做一件非常非常错误的事情。让我们具体明确通过方程16-4所描述的滤波器内核位于您的计算机阵列的哪里。作为一个例子，M 会选择100。请记住，M 必须是偶数。滤波器核心中的第一点是在阵列位置0，而最后一点是在数组100的位置。这意味着整个信号是101点长。中心的对称点为50，即M / 2。 50点到50点左边相对于50点右边是对称的。点0和点100具有相同的价值，同样点49和点51为相同。如果您必须在滤波器内核中明确的样本数量，如使用了FFT ，只需添加零到一端或其他。例如，与M = 100，你可以通过将127等于零得到101个样品点，在滤波器内核形成128点长。

图16-4

例如滤波器的内核和相应的阶跃响应。该正弦振荡频率约等于截止频率，c f ，而M 决定了内核的长度。

图16-4显示了窗式正弦滤波器内核的例子，及其相应的阶跃响应。在滤波器内核开始和结束的时候样本非常的小，甚至不能在图看到。不要错误的认为它们是不重要！这些样品的数值可能是很小，但他们集体作用对滤波器的性能有很大影响。这也是为什么浮点表示通常是用于实现窗式正弦滤波器。整数通常没有足够的动态范围捕捉滤波器内核中包含的值的大变化。窗式正弦滤波器如何在时域执行？太可怕了！这一阶跃响应，由于含有信息编码信号在时域中这不是一个滤波器

原文： CHAPTER

16 Windowed-Sinc Filters

Windowed-sinc filters are used to separate one band of frequencies from another. They are very stable, produce few surprises, and can be pushed to incredible performance levels. These exceptional frequency domain characteristics are obtained at the expense of poor performance in the time domain, including excessive ripple and overshoot in the stepe response. When carried out by standard convolution, windowed-sinc filters are easy to program, but slow to execute. Chapter 18 shows how the FFT can be used to dramatically improve the computational speed of these filters.

Strategy of the Windowed-Sinc

Figure 16-1 illustrates the idea behind the windowed-sinc filter. In (a), the

frequency response of the ideal low-pass filter is shown. All frequencies below the cutoff frequency, c f , are passed with unity amplitude, while all higher C frequencies

are blocked. The passband is perfectly flat, the attenuation in the stopband is infinite, and the transition between the two is infinitesimally small. Taking the Inverse Fourier Transform of this ideal frequency response produces the ideal filter kernel (impulse response) shown in (b). As previously discussed (see Chapter 11, Eq. 11-4), this curve is of the general form: sin(x )/x , called the sinc function , given by:

sin(2)[]c f i h i i ππ

= Convolving an input signal with this filter kernel provides a perfect low-pass filter. The problem is, the sinc function continues to both negative and positive infinity without dropping to zero amplitude. While this infinite length is not a problem for mathematics , it is a show stopper for computers .

To get around this problem, we will make two modifications to the sinc function in (b), resulting in the waveform shown in (c). First, it is truncated to M +1 points, symmetrically chosen around the main lobe, where M is an even number. All samples outside these M +1 points are set to zero, or simply ignored. Second, the entire sequence is shifted to the right so that it runs from 0 to M . This allows the filter kernel to be represented using only positive indexes. While many programming languages allow negative indexes, they area nuisance to use. The sole effect of this M /2 shift in the filter kernel is toshift the output signal by the same amount.

Since the modified filter kernel is only an approximation to the ideal filter kernel, it will not have an ideal frequency response. To find the frequency response that is obtained, the Fourier transform can be taken of the signal in (c), resulting in the curve in (d). It's a mess! There is excessive ripple in the passband and poor attenuation in the stopband (recall the Gibbs effect discussed in Chapter 11). These problems result

from the abrupt discontinuity at the ends of the truncated sinc function. Increasing the length of the filter kernel does not reduce these problems; the discontinuity is significant no matter how long M is made.

Fortunately, there is a simple method of improving this situation. Figure (e) shows a smoothly tapered curve called a Blackman window . Multiplying the truncated-sinc, (c), by the Blackman window, (e), results in the windowedsinc filter kernel shown in (f). The idea is to reduce the abruptness of the truncated ends and thereby improve the frequency response. Figure (g) shows

this improvement. The passband is now flat, and the stopband attenuation is so good it cannot be seen in this graph.

Several different windows are available, most of them named after their original developers in the 1950s. Only two are worth using, the Hammingwindow and the Blackman window These are given by:

EQUATION 16-1 The Hamming window. These windows run from 0i = to M ,

for a total of 1M + points

[]0.540.46cos(2/)w i M π=-

EQUATION 16-2 The Blackman window.

[]0.420.5cos(2/)0.08cos(4/)w i i M i M ππ=-+

Figure 16-2a shows the shape of these two windows for M =50 (i.e., 51 total points in the curves). Which of these two windows should you use? It's a trade-off between parameters. As shown in Fig. 16-2b, the Hamming window has about a 20% faster roll-off than the Blackman. However,

FIGURE 16-1 (facing page)

Derivation of the windowed-sinc filter kernel. The frequency response of the ideal low-pass filter is shown in (a), with the corresponding filter kernel in (b), a sinc function. Since the sinc is infinitely long, it must be truncated to be used in a computer, as shown in (c). However, this truncation results in undesirable changes in the frequency response, (d). The solution is to multiply the truncated-sinc with a smooth window, (e), resulting in the windowed -sinc filter kernel, (f). The frequency response of the windowed-sinc, (g), is smooth and well behaved. These figures are not to scale.

Chapter 16- Windowed-Sinc Filters

The Scientist and Engineer's Guide to Digital Signal Processing

FIGURE 16-2 Characteristics of the Blackman and Hamming windows. The shapes of these two windows are shown in (a), and given by Eqs. 16-1

and 16-2. As

shown in (b), the Hamming window

results in about

20% faster roll-off than the Blackman

window.

However, the Blackman window has

better stopband

attenuation (Blackman: 0.02%,

Hamming:

0.2%), and a lower passband ripple (Blackman:

0.02% Hamming: 0.2%).

(c) shows that the Blackman has a better stopband attenuation . To be exact, the stopband attenuation for the Blackman is -74dB (-0.02%), while the Hamming is only -53dB (-0.2%). Although it cannot be seen in these graphs, the Blackman has a passband ripple of only about 0.02%, while the Hamming is typically 0.2%. In general, the Blackman should be your first choice; a slow roll-off is easier to handle than poor stopband attenuation.

There are other windows you might hear about, although they fall short of the Blackman and Hamming. The Bartlett window is a triangle, using straight lines for the taper. The Hanning window, also called the raised cosinewindow , is given by: []0.50.5cos(2/)w i M π=-.

These two windows have about the same roll-off speed as the Hamming, but worse stopband attenuation (Bartlett: -25dB or 5.6%, Hanning -44dB or 0.63%). You might also hear of a rectangular window . This is the same as no window, just a truncation of the tails (such as in Fig. 16-1c). While the roll-off is -2.5 times faster than the Blackman, the stopband attenuation is only -21dB (8.9%).

Designing the Filter

To design a windowed-sinc, two parameters must be selected: the cutoff frequency, c f , and the length of the filter kernel, M . The cutoff frequency C

Chapter 16- Windowed-Sinc Filters

FIGURE 16-3

Filter length vs. roll-off of the windowed-sinc filter. As shown in (a), for M = 20, 40, and 200, the transition

bandwidths are BW = 0.2, 0.1, and 0.02 of the sampling rate, respectively. As shown in (b), the shape of the

frequency response does not change with different cutoff frequencies. In (b), M = 60.

is expressed as a fraction of the sampling rate, and therefore must be between 0 and 0.5. The value for M sets the roll-off according to the approximation:

EQUATION 16-3 Filter length vs. roll-off. The length of thefilter kernel, M , determines the transitionbandwidth of the filter, BW. This is only an

4M BW

particular window being used. approximation since roll-off depends on the

where BW is the width of the transition band, measured from where the curve just barely leaves one, to where it almost reaches zero (say, 99% to 1% of the curve). The transition bandwidth is also expressed as a fraction of the sampling frequency, and must between 0 and 0.5. Figure 16-3a shows an example of how this approximation is used. The three curves shown are generated from filter kernels with: M = 20, 40, and 200. From Eq. 16-3, the transition bandwidths are: BW =0.2, 0.1, and 0.02 , respectively. Figure (b) shows that the shape of the frequency response does not depend on the cutoff frequency selected.

Since the time required for a convolution is proportional to the length of the signals, Eq. 16-3 expresses a trade-off between computation time (depends on the value of M ) and filter sharpness (the value of BW ). For instance, the 20% slower roll-off of the Blackman window (as compared with the Hamming) can be compensated for by using a filter kernel 20% longer. In other words, it could be said that the Blackman window is 20% slower to execute that an equivalent roll-off Hamming window. This is

important because the execution speed of windowed-sinc filters is already terribly slow.

As also shown in Fig. 16-3b, the cutoff frequency of the windowed-sinc filter is measured at the one-half amplitude point. Why use 0.5 instead of the standard 0.707 (-3dB) used in analog electronics and other digital filters? This is because the windowed-sinc's frequency response is symmetrical between the passband and the stopband. For instance, the Hamming window results in a passband ripple of 0.2%, and an identical stopband attenuation (i.e., ripple in the stopband) of 0.2%. Other filters do not show this symmetry, and therefore have no advantage in using the one-half amplitude point to mark the cutoff frequency. As shown later in this chapter, this symmetry makes the windowedsinc ideal for spectral inversion .

After c f and M have been selected, the filter kernel is calculated from the calculated

from the relation:

sin(2(/2))24[][0.420.5cos()0.08cos()]/2c f i M i i h i k i M M M

πππ-=-+- EQUATION 16-4

The windowed-sinc filter kernel. The cutoff frequency, c f is expressed as a fraction

of the sampling rate, a value between 0 and 0.5. The length of the filterkernel is determined by M , which must be an even integer. The sample number i , is an integer that runs from 0 to M , resulting in M +1 total points in the filterkernel. The constant, K , is chosen to provide unity gain at zero frequency. To avoid a divide-by-zero error, for /2i M =, use []2c h i f K π=

Don't be intimidated by this equation! Based on the previous discussion, you should be able to identify three components: the sinc function , the M/2 shift , and the Blackman window . For the filter to have unity gain at DC, the constant K must be chosen such that the sum of all the samples is equal to one. In practice, ignore K during the calculation of the filter kernel, and then normalize all of the samples as needed. The program listed in Table 16-1 shows how this is done. Also notice how the calculation is handled at the center of the sinc, i = M /2, which involves a division by zero.

This equation may be long, but it is easy to use; simply type it into your computer program and forget it. Let the computer handle the calculations. If you find yourself trying to evaluate this equation by hand, you are doing something very very wrong. Let's be specific about where the filter kernel described by Eq. 16-4 is located in your computer array. As an example, M will be chosen to be 100. Remember, M must be an even number. The first point in the filter kernel is in array location 0, while the last point is in array location 100. This means that the entire signal is 101 points long. The center of symmetry is at point 50, i.e., M /2 . The 50 points to the left of point 50 are symmetrical with the 50 points to the right. Point 0 is the same value as point 100, and point 49 is the same as point 51. If you must have a specific number of samples in the

filter kernel, such as to use the FFT, simply add zeros to one end or the other. For example, with M =100, you could make samples 101 through 127 equal to zero, resulting in a filter kernel 128 points long.

Chapter 16- Windowed-Sinc Filters

FIGURE 16-4

Example filter kernels and the corresponding step responses. The frequency of the

f, while M sinusoidal oscillation is approximately equal to the cutoff frequency,

c

determines the kernel length.

Figure 16-4 shows examples of windowed-sinc filter kernels, and their corresponding step responses. The samples at the beginning and end of the filter kernels are so small that they can't even be seen in the graphs. Don't make the mistake of thinking they are

unimportant! These samples may be small in value; however, they collectively have a large effect on the performance of the filter. This is also why floating point representation is typically used to implement windowed-sinc filters. Integers usually don't have enough dynamic range to capture the large variation of values contained in the filter kernel. How does the windowed-sinc filter perform in the time domain? Terrible! The step response has overshoot and ringing; this is not a filter for signals with information encoded in the time domain.

最新The_Monster课文翻译

Deems Taylor: The Monster 怪才他身材矮小，头却很大，与他的身材很不相称——是个满脸病容的矮子。他神经兮兮，有皮肤病，贴身穿比丝绸粗糙一点的任何衣服都会使他痛苦不堪。而且他还是个夸大妄想狂。他是个极其自负的怪人。除非事情与自己有关，否则他从来不屑对世界或世人瞧上一眼。对他来说，他不仅是世界上最重要的人物，而且在他眼里，他是惟一活在世界上的人。他认为自己是世界上最伟大的戏剧家之一、最伟大的思想家之一、最伟大的作曲家之一。听听他的谈话，仿佛他就是集莎士比亚、贝多芬、柏拉图三人于一身。想要听到他的高论十分容易，他是世上最能使人筋疲力竭的健谈者之一。同他度过一个夜晚，就是听他一个人滔滔不绝地说上一晚。有时，他才华横溢；有时，他又令人极其厌烦。但无论是妙趣横生还是枯燥无味，他的谈话只有一个主题：他自己，他自己的所思所为。他狂妄地认为自己总是正确的。任何人在最无足轻重的问题上露出丝毫的异议，都会激得他的强烈谴责。他可能会一连好几个小时滔滔不绝，千方百计地证明自己如何如何正确。有了这种使人耗尽心力的雄辩本事，听者最后都被他弄得头昏脑涨，耳朵发聋，为了图个清静，只好同意他的说法。他从来不会觉得，对于跟他接触的人来说，他和他的所作所为并不是使人产生强烈兴趣而为之倾倒的事情。他几乎对世间的任何领域都有自己的理

论，包括素食主义、戏剧、政治以及音乐。为了证实这些理论，他写小册子、写信、写书……文字成千上万，连篇累牍。他不仅写了，还出版了这些东西——所需费用通常由别人支付——而他会坐下来大声读给朋友和家人听，一读就是好几个小时。他写歌剧，但往往是刚有个故事梗概，他就邀请——或者更确切说是召集——一群朋友到家里，高声念给大家听。不是为了获得批评，而是为了获得称赞。整部剧的歌词写好后，朋友们还得再去听他高声朗读全剧。然后他就拿去发表，有时几年后才为歌词谱曲。他也像作曲家一样弹钢琴，但要多糟有多糟。然而，他却要坐在钢琴前，面对包括他那个时代最杰出的钢琴家在内的聚会人群，一小时接一小时地给他们演奏，不用说，都是他自己的作品。他有一副作曲家的嗓子，但他会把著名的歌唱家请到自己家里，为他们演唱自己的作品，还要扮演剧中所有的角色。他的情绪犹如六岁儿童，极易波动。心情不好时，他要么用力跺脚，口出狂言，要么陷入极度的忧郁，阴沉地说要去东方当和尚，了此残生。十分钟后，假如有什么事情使他高兴了，他就会冲出门去，绕着花园跑个不停，或者在沙发上跳上跳下或拿大顶。他会因爱犬死了而极度悲痛，也会残忍无情到使罗马皇帝也不寒而栗。他几乎没有丝毫责任感。他似乎不仅没有养活自己的能力，也从没想到过有这个义务。他深信这个世界应该给他一条活路。为了支持这一信念，他

新版人教版高中语文课本的目录。

必修一阅读鉴赏第一单元1.*沁园春?长沙……………………………………毛泽东3 2.诗两首雨巷…………………………………………戴望舒6 再别康桥………………………………………徐志摩8 3.大堰河--我的保姆………………………………艾青10 第二单元4.烛之武退秦师………………………………….《左传》16 5.荆轲刺秦王………………………………….《战国策》18 6.*鸿门宴……………………………………..司马迁22 第三单元7.记念刘和珍君……………………………………鲁迅27 8.小狗包弟……………………………………….巴金32 9.*记梁任公先生的一次演讲…………………………梁实秋36 第四单元10.短新闻两篇别了，“不列颠尼亚”…………………………周婷杨兴39 奥斯维辛没有什么新闻…………………………罗森塔尔41 11.包身工………………………………………..夏衍44 12.*飞向太空的航程……………………….贾永曹智白瑞雪52 必修二阅读鉴赏第一单元1.荷塘月色…………………………………..朱自清2.故都的秋…………………………………..郁达夫3.*囚绿记…………………………………..陆蠡第二单元4.《诗经》两首氓采薇5.离骚………………………………………屈原6.*《孔雀东南飞》(并序) 7.*诗三首涉江采芙蓉《古诗十九首》短歌行……………………………………曹操归园田居(其一)…………………………..陶渊明第三单元8.兰亭集序……………………………………王羲之9.赤壁赋……………………………………..苏轼10.*游褒禅山记………………………………王安石第四单元11.就任北京大学校长之演说……………………..蔡元培12.我有一个梦想………………………………马丁?路德?金1 3.*在马克思墓前的讲话…………………………恩格斯第三册阅读鉴赏第一单元1.林黛玉进贾府………………………………….曹雪芹2.祝福………………………………………..鲁迅3. *老人与海…………………………………….海明威第二单元4.蜀道难……………………………………….李白5.杜甫诗三首秋兴八首(其一) 咏怀古迹(其三) 登高6.琵琶行(并序)………………………………..白居易7.*李商隐诗两首锦瑟马嵬(其二) 第三单元8.寡人之于国也…………………………………《孟子》9.劝学……………………………………….《荀子》10.*过秦论…………………………………….贾谊11.*师说………………………………………韩愈第四单元12.动物游戏之谜………………………………..周立明13.宇宙的边疆………………………………….卡尔?萨根14.*凤蝶外传……………………………………董纯才15.*一名物理学家的教育历程……………………….加来道雄第四册阅读鉴赏第一单元1.窦娥冤………………………………………..关汉卿2.雷雨………………………………………….曹禹3.*哈姆莱特……………………………………莎士比亚第二单元4.柳永词两首望海潮(东南形胜) 雨霖铃(寒蝉凄切) 5.苏轼词两首念奴娇?赤壁怀古定风波(莫听穿林打叶声) 6.辛弃疾词两首水龙吟?登建康赏心亭永遇乐?京口北固亭怀古7.*李清照词两首醉花阴(薄雾浓云愁永昼) 声声慢(寻寻觅觅) 第三单元8.拿来主义……………………………………….鲁迅9.父母与孩子之间的爱……………………………..弗罗姆10.*短文三篇热爱生

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基于MATLAB实时串口数据采集与曲线显示

全日制普通本科生毕业设计 基于MATLAB实时串口数据采集与曲线显示REAL-TIME SERIAL DATA ACQUISITION AND FIGURE SHOW BASED ON MATLAB 学生姓名： 学号： 年级专业及班级： 指导老师及职称： 学院： 提交日期：2011年5月

全日制普通本科生毕业论文（设计） 诚信声明 本人郑重声明：所呈交的本科毕业论文（设计）是本人在指导老师的指导下，进行研究工作所取得的成果，成果不存在知识产权争议。除文中已经注明引用的内容外，本论文不含任何其他个人或集体已经发表或撰写过的作品成果。对本文的研究做出重要贡献的个人和集体在文中均作了明确的说明并表示了谢意。本人完全意识到本声明的法律结果由本人承担。 毕业论文（设计）作者签名： 年月日

目录 摘要 (1) 关键词 (1) 1前言 (2) 1.1 Matlab实时串口数据采集研究现状及发展趋势 (2) 1.2研究的目的和意义 (4) 1.3论文的组织结构 (5) 2Matlab下实时串口数据采集概要 (5) 2.1 Matlab的Serial类 (5) 2.2 数据采集 (6) 2.3曲线显示 (7) 3实时串口数据采集与曲线显示的实现 (8) 3．1实时串口通信的实现 (8) 3.2数据采集的实现 (9) 3.3曲线显示GUI的实现 (10) 4基于MATLAB的实时串口数据采集与曲线显示的具体做法 (12) 4.1数据采集的一般流程 (12) 4.1.1创建接口对象并设置属性 (12) 4.1.2打开串口设备对象 (12) 4.1.3读写串口操作 (13) 4.1.4关闭并清除设备对象 (13) 4.2基于Matlab中断方式的实时串行通信编程 (13) 4.3绘制采集数据的曲线波形和数据显示 (14) 4.3.1绘制曲线波形 (14)

高中外研社英语选修六Module5课文Frankenstein's Monster

Frankenstein's Monster Part 1 The story of Frankenstein Frankenstein is a young scientist/ from Geneva, in Switzerland. While studying at university, he discovers the secret of how to give life/ to lifeless matter. Using bones from dead bodies, he creates a creature/ that resembles a human being/ and gives it life. The creature, which is unusually large/ and strong, is extremely ugly, and terrifies all those/ who see it. However, the monster, who has learnt to speak, is intelligent/ and has human emotions. Lonely and unhappy, he begins to hate his creator, Frankenstein. When Frankenstein refuses to create a wife/ for him, the monster murders Frankenstein's brother, his best friend Clerval, and finally, Frankenstein's wife Elizabeth. The scientist chases the creature/ to the Arctic/ in order to destroy him, but he dies there. At the end of the story, the monster disappears into the ice/ and snow/ to end his own life. Part 2 Extract from Frankenstein It was on a cold November night/ that I saw my creation/ for the first time. Feeling very anxious, I prepared the equipment/ that would give life/ to the thing/ that lay at my feet. It was already one/ in the morning/ and the rain/ fell against the window. My candle was almost burnt out when, by its tiny light，I saw the yellow eye of the creature open. It breathed hard, and moved its arms and legs. How can I describe my emotions/ when I saw this happen? How can I describe the monster who I had worked/ so hard/ to create? I had tried to make him beautiful. Beautiful! He was the ugliest thing/ I had ever seen! You could see the veins/ beneath his yellow skin. His hair was black/ and his teeth were white. But these things contrasted horribly with his yellow eyes, his wrinkled yellow skin and black lips. I had worked/ for nearly two years/ with one aim only, to give life to a lifeless body. For this/ I had not slept, I had destroyed my health. I had wanted it more than anything/ in the world. But now/ I had finished, the beauty of the dream vanished, and horror and disgust/ filled my heart. Now/ my only thoughts were, "I wish I had not created this creature, I wish I was on the other side of the world, I wish I could disappear!” When he turned to look at me, I felt unable to stay in the same room as him. I rushed out, and /for a long time/ I walked up and down my bedroom. At last/ I threw myself on the bed/ in my clothes, trying to find a few moments of sleep. But although I slept, I had terrible dreams. I dreamt I saw my fiancée/ walking in the streets of our town. She looked well/ and happy/ but as I kissed her lips，they became pale, as if she were dead. Her face changed and I thought/ I held the body of my dead mother/ in my arms. I woke, shaking with fear. At that same moment，I saw the creature/ that I had created. He was standing/by my bed/ and watching me. His

人教版高中语文必修必背课文精编WORD版

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必修1 沁园春·长沙（全文）毛泽东 独立寒秋， 湘江北去， 橘子洲头。 看万山红遍， 层林尽染， 漫江碧透， 百舸争流。 鹰击长空， 鱼翔浅底， 万类霜天竞自由。 怅寥廓， 问苍茫大地， 谁主沉浮。 携来百侣曾游， 忆往昔峥嵘岁月稠。

恰同学少年， 风华正茂， 书生意气， 挥斥方遒。 指点江山， 激扬文字， 粪土当年万户侯。 曾记否， 到中流击水， 浪遏飞舟。 雨巷（全文）戴望舒撑着油纸伞，独自 彷徨在悠长、悠长 又寂寥的雨巷， 我希望逢着 一个丁香一样地

结着愁怨的姑娘。 她是有 丁香一样的颜色， 丁香一样的芬芳， 丁香一样的忧愁， 在雨中哀怨， 哀怨又彷徨； 她彷徨在这寂寥的雨巷，撑着油纸伞 像我一样， 像我一样地 默默彳亍着 冷漠、凄清，又惆怅。她默默地走近， 走近，又投出 太息一般的眼光

她飘过 像梦一般地， 像梦一般地凄婉迷茫。像梦中飘过 一枝丁香地， 我身旁飘过这个女郎；她默默地远了，远了，到了颓圮的篱墙， 走尽这雨巷。 在雨的哀曲里， 消了她的颜色， 散了她的芬芳， 消散了，甚至她的 太息般的眼光 丁香般的惆怅。 撑着油纸伞，独自

彷徨在悠长、悠长 又寂寥的雨巷， 我希望飘过 一个丁香一样地 结着愁怨的姑娘。 再别康桥（全文）徐志摩 轻轻的我走了，正如我轻轻的来； 我轻轻的招手，作别西天的云彩。 那河畔的金柳，是夕阳中的新娘； 波光里的艳影，在我的心头荡漾。 软泥上的青荇，油油的在水底招摇； 在康河的柔波里，我甘心做一条水草！ 那榆荫下的一潭，不是清泉， 是天上虹揉碎在浮藻间，沉淀着彩虹似的梦。寻梦？撑一支长篙，向青草更青处漫溯， 满载一船星辉，在星辉斑斓里放歌。

Matlab与51单片机的串口通信

数字信号处理2012电子信息工程专业答辩报告

Matlab与51单片机的串口通信 一、简介 从Matlab6.0版本开始，Mathworks公司在软件中增加了设备控制箱（instrument control toolbox），提供了对RS-232/RS-485通信标准串口（九针串口）通信正式支持（本实验采用USB转串口）利用该工具箱serial类及instrcallback()回调函数，能可靠地进行实时串地通信。Matlab支持面向对象技术，用一个对象将计算机串口封装起来，只要创建串口对象，对串口对象操作就是对串口操作，非常方便。使用serial函数就可创建串口对象，通过定义串口对象的属性，能定义串口的通信模式，从串口对象属性也能了解串口的状态，即可以通过MATLAB的串口通讯函数读写数据。 二、 Matlab串口函数 serial 创建一个串口对象，格式:s = serial('coml' ) fopen 打开串口对象，格式:fopen(s) fwrite 其他程序能对该串口进行读写操作fwrite（s,’’） fread 读取串口数据，格式: fread(s) fclose 关闭串口对象，格式:fclose(s) free 解除Matlab对串口对象的控制，使 delete 删除对象s，格式:delete(s) clear 从工作空间中删除对象s，格式:clear(s) 三、实现功能 利用MATLAB串口通信函数，读写51单片机（STC89C52Ｒ＋）数据，运用keil编写时钟程序，烧录到单片机中，时钟程序实现的功能是实现时钟的显示，并且能用开发板上的三个按钮进行时钟的修改，一个按钮进入修改模式（复位），另两个实现时间的增减。编辑MATLAB程序，实现对单片机的控制。读写串口操作。初始化并打开串口调协对象之后，现在可以对串口设备对象进行读写操作，串口读写操作支持二进制和文本（ASCII）两种方式。当Matlab通信数据采用西方（ASCII）方式时，读写串口设备命令分别是fscanf、fpritf；当Matlab通信数据采用二进制方式时，读写串口设备命令分别是fread、fwrite。

RTTOV-7 Users Guide

RTTOV-7 Users Guide Roger Saunders Room 408, Met Office London Rd., Bracknell Berks, RG12 2SZ U.K. This documentation was developed within the context of the EUMETSAT Satellite Application Facility on Numerical Weather Prediction (NWP SAF), under the Cooperation Agreement dated 25 November 1998, between EUMETSAT and the Met Office, UK, by one or more partners within the NWP SAF. The partners in the NWP SAF are the Met Office, ECMWF, KNMI and Météo France. Copyright 2002, EUMETSAT, All Rights Reserved. Change record Version Date Author / changed by Remarks 111/12/01R. Saunders Initial draft to code developers for comments 231/01/02R Saunders Modified draft after comments 313/03/02R Saunders Modified after comments from J Eyre and MTR RIDs 427/05/02R Saunders Corrected IFAIL documentation

(完整版)Unit7TheMonster课文翻译综合教程四

Unit 7 The Monster Deems Taylor 1He was an undersized little man, with a head too big for his body ― a sickly little man. His nerves were bad. He had skin trouble. It was agony for him to wear anything next to his skin coarser than silk. And he had delusions of grandeur. 2He was a monster of conceit. Never for one minute did he look at the world or at people, except in relation to himself. He believed himself to be one of the greatest dramatists in the world, one of the greatest thinkers, and one of the greatest composers. To hear him talk, he was Shakespeare, and Beethoven, and Plato, rolled into one. He was one of the most exhausting conversationalists that ever lived. Sometimes he was brilliant; sometimes he was maddeningly tiresome. But whether he was being brilliant or dull, he had one sole topic of conversation: himself. What he thought and what he did. 3He had a mania for being in the right. The slightest hint of disagreement, from anyone, on the most trivial point, was enough to set him off on a harangue that might last for hours, in which he proved himself right in so many ways, and with such exhausting volubility, that in the end his hearer, stunned and deafened, would agree with him, for the sake of peace. 4It never occurred to him that he and his doing were not of the most intense and fascinating interest to anyone with whom he came in contact. He had theories about almost any subject under the sun, including vegetarianism, the drama, politics, and music; and in support of these theories he wrote pamphlets, letters, books ... thousands upon thousands of words, hundreds and hundreds of pages. He not only wrote these things, and published them ― usually at somebody else’s expense ― but he would sit and read them aloud, for hours, to his friends, and his family. 5He had the emotional stability of a six-year-old child. When he felt out of sorts, he would rave and stamp, or sink into suicidal gloom and talk darkly of going to the East to end his days as a Buddhist monk. Ten minutes later, when something pleased him he would rush out of doors and run around the garden, or jump up and down off the sofa, or stand on his head. He could be grief-stricken over the death of a pet dog, and could be callous and heartless to a degree that would have made a Roman emperor shudder. 6He was almost innocent of any sense of responsibility. He was convinced that

人教版高中语文必修一背诵篇目

高中语文必修一背诵篇目 1、《沁园春长沙》毛泽东 独立寒秋，湘江北去，橘子洲头。 看万山红遍，层林尽染；漫江碧透，百舸争流。 鹰击长空，鱼翔浅底，万类霜天竞自由。 怅寥廓，问苍茫大地，谁主沉浮？ 携来百侣曾游，忆往昔峥嵘岁月稠。 恰同学少年，风华正茂；书生意气，挥斥方遒。 指点江山，激扬文字，粪土当年万户侯。 曾记否，到中流击水，浪遏飞舟？ 2、《诗两首》 （1）、《雨巷》戴望舒 撑着油纸伞，独自 /彷徨在悠长、悠长/又寂寥的雨巷， 我希望逢着 /一个丁香一样的 /结着愁怨的姑娘。 她是有 /丁香一样的颜色，/丁香一样的芬芳， /丁香一样的忧愁， 在雨中哀怨， /哀怨又彷徨； /她彷徨在这寂寥的雨巷， 撑着油纸伞 /像我一样， /像我一样地 /默默彳亍着 冷漠、凄清，又惆怅。 /她静默地走近/走近，又投出 太息一般的眼光，/她飘过 /像梦一般地， /像梦一般地凄婉迷茫。 像梦中飘过 /一枝丁香的， /我身旁飘过这女郎； 她静默地远了，远了，/到了颓圮的篱墙， /走尽这雨巷。 在雨的哀曲里， /消了她的颜色， /散了她的芬芳， /消散了，甚至她的 太息般的眼光， /丁香般的惆怅/撑着油纸伞，独自 /彷徨在悠长，悠长 又寂寥的雨巷， /我希望飘过 /一个丁香一样的 /结着愁怨的姑娘。 （2）、《再别康桥》徐志摩 轻轻的我走了， /正如我轻轻的来； /我轻轻的招手， /作别西天的云彩。 那河畔的金柳， /是夕阳中的新娘； /波光里的艳影， /在我的心头荡漾。 软泥上的青荇， /油油的在水底招摇； /在康河的柔波里， /我甘心做一条水草！那榆阴下的一潭， /不是清泉，是天上虹 /揉碎在浮藻间， /沉淀着彩虹似的梦。寻梦？撑一支长篙， /向青草更青处漫溯， /满载一船星辉， /在星辉斑斓里放歌。但我不能放歌， /悄悄是别离的笙箫； /夏虫也为我沉默， / 沉默是今晚的康桥！悄悄的我走了， /正如我悄悄的来；/我挥一挥衣袖， /不带走一片云彩。 4、《荆轲刺秦王》 太子及宾客知其事者，皆白衣冠以送之。至易水上，既祖，取道。高渐离击筑，荆轲和而歌，为变徵之声，士皆垂泪涕泣。又前而为歌曰：“风萧萧兮易水寒，壮士一去兮不复还！”复为慷慨羽声，士皆瞋目，发尽上指冠。于是荆轲遂就车而去，终已不顾。 5、《记念刘和珍君》鲁迅 （1）、真的猛士，敢于直面惨淡的人生，敢于正视淋漓的鲜血。这是怎样的哀痛者和幸福者？然而造化又常常为庸人设计，以时间的流驶，来洗涤旧迹，仅使留下淡红的血色和微漠的悲哀。在这淡红的血色和微漠的悲哀中，又给人暂得偷生，维持着这似人非人的世界。我不知道这样的世界何时是一个尽头！

matlab串口实时波形显示

作者：GG 功能：实现matalb与PC外设通讯 本例：串口232与外设单片机51通讯。实时监控51数据并且实时图形显示 时间：2011—9—16 简介：实现该功能使用M脚本文件和函数文件。 第一个文件连接串口和打开串口，设置了串口的一些参数和触发事件。连接串口COM5。有关该方面的知识请自行百度I/O文字流。 第二个文件是时间回调函数，相当于其他语言中例如C语言的中断函数 第三文件是关闭串口和清除列连接。并且清除中间TXT中介文件内容 下面是源文件 第一个： clear all s=serial('COM5');%打开串口 s.BytesAvailableFcnMode='byte';%设置事件触发为接受触发 s.InputBufferSize=5000;%设置接受缓冲区大小为5000个字节 s.BytesAvailableFcnCount=10;%每次接受到50个数据时候触发事件 s.BaudRate=19200;%设置通讯波特率 s.BytesAvailableFcn=@my_callback;%指向触发事件函数 fopen(s);%打开串口 第二个 function my_callback(obj,event) out=fread(obj,10,'uint8');%串口处读出50个数据 fid=fopen('G1.txt','a+');%打开文件并且追加 fprintf(fid,'%3d',out); fclose(fid); speed=textread('G1.txt','%u'); plot(speed); disp('save ok!'); end 第三个 fclose(s);%关闭串口 delete(s);%删除串口变量 clear all; fid=fopen('G1.txt','w');%清除中间文件txt a=[]; fprintf(fid,'%s',a); fclose(fid); clear all;%清除所以变量

人教版高中语文必修必背课文

必修1 沁园春·长沙（全文）毛泽东 独立寒秋， 湘江北去， 橘子洲头。 看万山红遍， 层林尽染， 漫江碧透， 百舸争流。 鹰击长空， 鱼翔浅底， 万类霜天竞自由。 怅寥廓， 问苍茫大地， 谁主沉浮。 携来百侣曾游， 忆往昔峥嵘岁月稠。 恰同学少年， 风华正茂， 书生意气， 挥斥方遒。 指点江山， 激扬文字， 粪土当年万户侯。 曾记否， 到中流击水， 浪遏飞舟。 雨巷（全文）戴望舒 撑着油纸伞，独自 彷徨在悠长、悠长 又寂寥的雨巷， 我希望逢着 一个丁香一样地 结着愁怨的姑娘。 她是有 丁香一样的颜色， 丁香一样的芬芳， 丁香一样的忧愁， 在雨中哀怨， 哀怨又彷徨；

她彷徨在这寂寥的雨巷， 撑着油纸伞 像我一样， 像我一样地 默默彳亍着 冷漠、凄清，又惆怅。 她默默地走近， 走近，又投出 太息一般的眼光 她飘过 像梦一般地， 像梦一般地凄婉迷茫。 像梦中飘过 一枝丁香地， 我身旁飘过这个女郎； 她默默地远了，远了， 到了颓圮的篱墙， 走尽这雨巷。 在雨的哀曲里， 消了她的颜色， 散了她的芬芳， 消散了，甚至她的 太息般的眼光 丁香般的惆怅。 撑着油纸伞，独自 彷徨在悠长、悠长 又寂寥的雨巷， 我希望飘过 一个丁香一样地 结着愁怨的姑娘。 再别康桥（全文）徐志摩 轻轻的我走了，正如我轻轻的来；我轻轻的招手，作别西天的云彩。 那河畔的金柳，是夕阳中的新娘；波光里的艳影，在我的心头荡漾。 软泥上的青荇，油油的在水底招摇；

在康河的柔波里，我甘心做一条水草！ 那榆荫下的一潭，不是清泉， 是天上虹揉碎在浮藻间，沉淀着彩虹似的梦。 寻梦？撑一支长篙，向青草更青处漫溯， 满载一船星辉，在星辉斑斓里放歌。 但我不能放歌，悄悄是别离的笙箫； 夏虫也为我沉默，沉默是今晚的康桥。 悄悄的我走了，正如我悄悄的来； 我挥一挥衣袖，不带走一片云彩。 记念刘和珍君（二、四节）鲁迅 二 真的猛士，敢于直面惨淡的人生，敢于正视淋漓的鲜血。这是怎样的哀痛者和幸福者？然而造化又常常为庸人设计，以时间的流驶，来洗涤旧迹，仅使留下淡红的血色和微漠的悲哀。在这淡红的血色和微漠的悲哀中，又给人暂得偷生，维持着这似人非人的世界。我不知道这样的世界何时是一个尽头！ 我们还在这样的世上活着；我也早觉得有写一点东西的必要了。离三月十八日也已有两星期，忘却的救主快要降临了罢，我正有写一点东西的必要了。 四 我在十八日早晨，才知道上午有群众向执政府请愿的事；下午便得到噩耗，说卫队居然开枪，死伤至数百人，而刘和珍君即在遇害者之列。但我对于这些传说，竟至于颇为怀疑。我向来是不惮以最坏的恶意，来推测中国人的，然而我还不料，也不信竟会下劣凶残到这地步。况且始终微笑着的和蔼的刘和珍君，更何至于无端在府门前喋血呢？ 然而即日证明是事实了，作证的便是她自己的尸骸。还有一具，是杨德群君的。而且又证明着这不但是杀害，简直是虐杀，因为身体上还有棍棒的伤痕。 但段政府就有令，说她们是“暴徒”！ 但接着就有流言，说她们是受人利用的。 惨象，已使我目不忍视了；流言，尤使我耳不忍闻。我还有什么话可说呢？我懂得衰亡民族之所以默无声息的缘由了。沉默啊，沉默啊！不在沉默中爆发，就在沉默中灭亡。

matlab串口通信

摘要：结合单片机和Matlab两者优点，基于事件驱动中断通信机制，提出一种Matlab环境下PC机与单片机实时串行通信及数据处理方法；完成单片机数据采集系统与PC机RS-232/RS-485串行通信及其通信数据分析处理、文件存储、FIR滤波及图形显示；简化系统开发流程，提高开发效率。该方法已成功应用于一个PIC16F876单片机应用系统实例之中。 关键词：PIC16F876 Matlab 串口通信 RS-232 事件驱动回调函数 引言 Matlab是由美国Mathworks公司开发面向理论分析研究、工程计算数据处理和缓图一套具有强大功能软件系统。其中Matlab语言是一种以矩阵为基本运算单元解释执行高级语言，编程简例，只要几条语句就能实现诸如FFT变换、FIR/IIR滤波等数据分析处理，易于掌握。从Matlab6.0版本开始，Mathworks 公司在软件中增加了设备控制箱（instrument control toolbox），提供了对RS-232/RS-485通信标准串口通信正式支持。利用该工具箱serial类及instrcallback()回调函数，能可靠地进行实时串地通信。为此，笔者充分结合单片机和Matlab优点，基于事件驱动中断通信机制，提出了一种Matlab环境下PC机与单片机实时串行通信数据处理方法，极大地简化开发流程，提高了系统开发效率。另外，与目前普遍采用基于Matlab查询方式下非实时串行通信技术相比，这种方法实用性也大大增强了。 https://www.wendangku.net/doc/3812927881.html,提示请看下图: 1 系统总体设计简介 下面以Mircochip公司PIC16F876单片机为下位机，PC机为上位机组成实时数据采集处理系统为例，介绍基于Matlab环境下PC机与单片机串行通信实时数据处理方法实现。数据采集系统结构框图如图1所示。PC机串口与单片机USART 口通过MAX232电平转换芯片相连，系统工作时，Matlab通过调用设备控制工具箱中serial类及相关函数。来创建串口设备对象，得到设备文件句柄，从而以操作文件方式实现对PC机串行口读写操作。因而PC机可以通过Matlab向串行

Pages from EViews 6 Users Guide II

Appendix D. Estimation and Solution Options EViews estimates the parameters of a wide variety of nonlinear models, from nonlinear least squares equations, to maximum likelihood models, to GMM specifications. These types of nonlinear estimation problems do not have closed form solutions and must be estimated using iterative methods. EViews also solves systems of non-linear equations. Again, there are no closed form solutions to these problems, and EViews must use an iterative method to obtain a solution. Below, we provide details on the algorithms used by EViews in dealing with nonlinear esti-mation and solution, and the optional settings that we provide to allow you to control esti-mation. Our discussion here is necessarily brief. For additional details, we direct you to the quite readable discussions in Press, et al. (1992), Quandt (1983), Thisted (1988), and Amemiya (1983). Setting Estimation Options When you estimate an equation in EViews, you enter specification information into the Specification tab of the Equation Estimation dialog. Clicking on the Options tab displays a dialog that allows you to set various options to control the estimation procedure. The con-tents of the dialog will differ depending upon the options available for a particular estima-tion procedure. The default settings for the options will be taken from the global options (“Estimation Defaults” on page767), or from the options used previously to estimate the object. The Options tab for binary models is depicted here. For other estimator and estimation techniques (e.g. systems) the dialog will differ to reflect the different estimation options that are available. Starting Coefficient Val- ues Iterative estimation procedures require starting values for the coefficients of the model. There are no general rules for select-