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CS代写|图像处理作业代写Image Processing代考|COMP345

Posted on 2022年10月19日2022年10月19日 by statistics-lab

如果你也在怎样代写图像处理Image Processing这个学科遇到相关的难题，请随时右上角联系我们的24/7代写客服。

图像处理是使用数字计算机通过一种算法来处理数字图像。

statistics-lab™ 为您的留学生涯保驾护航在代写图像处理Image Processing方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写图像处理Image Processing代写方面经验极为丰富，各种代写图像处理Image Processing相关的作业也就用不着说。

我们提供的图像处理Image Processing及其相关学科的代写，服务范围广, 其中包括但不限于:

Statistical Inference 统计推断
Statistical Computing 统计计算
Advanced Probability Theory 高等楖率论
Advanced Mathematical Statistics 高等数理统计学
(Generalized) Linear Models 广义线性模型
Statistical Machine Learning 统计机器学习
Longitudinal Data Analysis 纵向数据分析
Foundations of Data Science 数据科学基础

CS代写|图像处理作业代写Image Processing代考|Half-Tone Output Technology

General printing equipment can only directly output binary images. For example, the grayscale output of a laser printer has only two levels (either printing, outputting black; or not printing, outputting white). To output a grayscale image on a binary image output device and maintain its original grayscale level, a technique called half-tone output is often used.
Half-tone output technology can be regarded as a technology that converts grayscale images into binary images. It converts various gray scales in the intended output image into a binary point mode so that the grayscale image can be output by a printing device that can only directly output binary points. At the same time, it takes advantage of the integrated characteristics of the human eye, by controlling the form of the output binary point pattern (including number, size, shape, etc.) to give people a visual sense of multiple gray levels. In other words, the image output by the half-tone output technology is still a binary image at a very fine scale, but due to the spatial local averaging effect of the eyes, what is perceived is a grayscale image at a coarser scale. For example, in a binary image, the gray level of each pixel is only white or black, but from a certain distance, the unit perceived by the human eye is composed of multiple pixels, then the gray level perceived by the human eye is the average gray level of all pixels in this unit (proportional to the number of black pixels).

Half-tone output technology is mainly divided into two types: amplitude modulation (AM) technology and frequency modulation (FM) technology, which will be introduced separately below.

In the beginning, the half-tone output technology proposed and used displays of different gray levels by adjusting the size of the output black dots, which can be called amplitude modulation (AM) half-tone output technology. For example, the pictures in the early newspapers used ink dots of different sizes on the grid to represent the gray scale. When viewed from a certain distance, a group of small ink dots can produce a brighter gray scale visual effect, while a group of large ink dots can produce a darker gray scale visual effect. In practice, the size of ink dots is inversely proportional to the gray scale being represented, that is, the dots printed in the bright image region are small, and the dots printed in the dark image region are larger. When the ink dot is small enough and the observation distance is long enough, the human eye can obtain a relatively continuous and smooth gray-scale image according to

the integrated characteristics. In general, the resolution of pictures in newspapers is about 100 dots per inch (DPI), while the resolution of pictures in books or magazines is about 300 DPI.

In amplitude modulation, the binary points are regularly arranged. The size of these dots varies according to the gray scale to be represented, and the shape of the dots is not a decisive factor. For example, on a laser printer, it simulates different gray scales by controlling the proportion of ink coverage, and the shape of the ink dots is not strictly controlled. When the amplitude modulation technology is used, the effect of the output binary point mode not only depends on the size of each point but also depends on the size of the grid interval. The smaller the interval, the higher the output resolution. The interval size of the grid is limited by the resolution of the printer (measured in DPI).

CS代写|图像处理作业代写Image Processing代考|Half-Tone Output Mask

A specific implementation method of half-tone output is to first subdivide the image output unit and combine the adjacent basic binary points to form the output unit so that each output unit contains several basic binary points. Let some basic binary points output black while other basic binary points output white to get different grayscale effects. In other words, to output different gray levels, a set of masks/templates needs to be established, and each mask corresponds to an output unit. Divide each mask into regular grids, and each grid corresponds to a basic binary point. By adjusting each basic binary point to black or white, each mask can output a different grayscale so as to achieve the purpose of outputting grayscale images.

If a mask is divided into $2 \times 2$ grids, five different gray levels can be output according to the way shown in Figure 1.7. If a mask is divided into $3 \times 3$ grids, ten different gray scales can be output according to the way shown in Figure 1.8. If a mask is divided into $4 \times 4$ grids, 17 different gray scales can be output according to the way shown in Figure 1.9. By analogy, if a mask is divided into $n \times n$ grids, then $n^2+1$ different gray levels can be output.
Because there are $C_k^n=n ! /(n-k) ! k !$ different methods for putting $k$ points into $n$ units, the arrangement of black points in these figures is not unique. Note that if a grid is black at a certain gray level, it will still be black in all outputs greater than that gray level.

Divide the mask into grids according to the above method, then to output 256 gray levels, a mask needs to be divided into $16 \times 16$ units, that is, $16 \times 16$ positions are used to represent one pixel. It can be seen that the spatial resolution of the output image will be greatly affected. It can be seen that the half-tone output technology is only worth using when the gray value output by the output device itself is limited, and it is a reduction in spatial resolution in exchange for an increase in amplitude resolution. Assuming that each pixel in a $2 \times 2$ matrix can be white or black, each pixel requires one bit. Regarding this $2 \times 2$ matrix as a half-tone output unit, this unit needs 4 bits and can output 5 gray scales ( 16 modes),which are $0 / 4,1 / 4,2 / 4,3 / 4$, and $4 / 4$ (or written as $0,1,2,3$, and 4). However, if a pixel is represented by four bits, the pixel can have 16 gray levels. From this point of view, when the half-tone output uses the same storage unit, if the number of output levels increases, the number of output units will decrease.

To maintain the sharpness of the details in the image, it is necessary to have more lines per inch; at the same time, to represent these details, it also needs to have more brightness levels. This requires the printer to be able to print a large number of very small dots. Dividing a template into $8 \times 8$ grids can print 65 gray scales. For printing at 125 lines per inch, this corresponds to $8 \times 125=1,000 \mathrm{dpi}$. In most applications, this is the lower limit of the printed image. Color printing requires smaller dots, and high-quality printing often requires $2,400-3,000$ dpi.

When outputting images on different media, the required resolutions are often different. For example, when an image is displayed on the screen, the number of rows per inch generally corresponds to the number of grids per inch. When displaying images in newspapers, a resolution of at least 85 lines per inch is often used; for magazines or books, a resolution of at least 133 lines or 175 lines per inch is often used.

图像处理代考

CS代写|图像处理作业代写Image Processing代考|Half-Tone Output Technology

一般的印刷设备只能直接输出二值图像。例如，激光打印机的灰度输出只有两个级别（打印，输出黑色；或不打印，输出白色）。为了在二进制图像输出设备上输出灰度图像并保持其原始灰度级，通常使用一种称为半色调输出的技术。
半色调输出技术可以看作是一种将灰度图像转换为二值图像的技术。它将预期输出图像中的各种灰度转换为二进制点模式，使灰度图像可以由只能直接输出二进制点的打印设备输出。同时，它利用人眼的综合特性，通过控制输出二进制点图案的形式（包括数量、大小、形状等），给人以多重灰度的视觉感受。也就是说，半色调输出技术输出的图像仍然是非常精细尺度的二值图像，但由于眼睛的空间局部平均效应，感知到的是较粗尺度的灰度图像。例如，在二值图像中，

半色调输出技术主要分为调幅（AM）技术和调频（FM）技术两种，下面分别介绍。

半色调输出技术最初是通过调整输出黑点的大小来提出和使用不同灰度的显示器，可称为调幅（AM）半色调输出技术。例如，早期报纸上的图片在网格上使用不同大小的墨点来表示灰度。从一定距离看，一组小墨点可以产生较亮的灰度视觉效果，而一组大墨点可以产生较暗的灰度视觉效果。在实际应用中，墨点的大小与所代表的灰度成反比，即在亮图像区域打印的墨点较小，在暗图像区域打印的墨点较大。当墨点足够小，观察距离足够长时，

综合特征。一般来说，报纸上图片的分辨率约为每英寸100点（DPI），而书籍或杂志上的图片分辨率约为300 DPI。

在幅度调制中，二进制点是规则排列的。这些点的大小根据要表示的灰度而变化，点的形状不是决定性因素。比如在激光打印机上，通过控制墨水覆盖的比例来模拟不同的灰度，对墨点的形状没有严格控制。使用幅度调制技术时，输出二进制点模式的效果不仅取决于每个点的大小，还取决于网格间隔的大小。间隔越小，输出分辨率越高。网格的间隔大小受打印机分辨率的限制（以 DPI 为单位）。

CS代写|图像处理作业代写Image Processing代考|Half-Tone Output Mask

半色调输出的一种具体实现方法是先对图像输出单元进行细分，将相邻的基本二进制点组合形成输出单元，使得每个输出单元包含若干个基本二进制点。让一些基本二进制点输出黑色，而其他基本二进制点输出白色，以获得不同的灰度效果。也就是说，要输出不同的灰度级，需要建立一组掩码/模板，每个掩码对应一个输出单元。将每个掩码分成规则的网格，每个网格对应一个基本的二进制点。通过将每个基本二进制点调整为黑色或白色，每个掩模可以输出不同的灰度，从而达到输出灰度图像的目的。

如果一个掩码分为2×2格，按照图 1.7 所示的方式可以输出五种不同的灰度。如果一个掩码分为3×3格，按照图 1.8 所示的方式可以输出十种不同的灰度。如果一个掩码分为4×4格，按照图 1.9 所示的方式可以输出 17 种不同的灰度。以此类推，如果一个面具被分为n×n格子，然后n2+1可以输出不同的灰度。
因为有Cķn=n!/(n−ķ)!ķ!不同的放置方法ķ指向n单位，这些图中黑点的排列并不是唯一的。请注意，如果网格在某个灰度级为黑色，则在所有大于该灰度级的输出中仍将是黑色。

按照上面的方法将mask划分成网格，那么要输出256个灰度级，需要将一个mask划分为16×16单位，即16×16位置用于表示一个像素。可以看出，输出图像的空间分辨率会受到很大影响。可见，半色调输出技术只有在输出设备本身输出的灰度值有限的情况下才值得使用，它是以空间分辨率的降低换取幅度分辨率的提高。假设每个像素在2×2矩阵可以是白色或黑色，每个像素需要一位。关于这个2×2矩阵作为半色调输出单元，该单元需要 4 位，可以输出 5 种灰度（16 种模式），分别是0/4,1/4,2/4,3/4，和4/4（或写成0,1,2,3, 和 4)。但是，如果一个像素用 4 位表示，则该像素可以有 16 个灰度级。由此看来，当半色调输出使用相同的存储单元时，如果输出级数增加，输出单元数会减少。

为了保持图像中细节的清晰度，每英寸必须有更多的线条；同时，为了表现这些细节，还需要有更多的亮度等级。这就要求打印机能够打印大量非常小的点。将模板划分为8×8网格可以打印65个灰度。对于以每英寸 125 行打印，这对应于8×125=1,000dp一世. 在大多数应用中，这是打印图像的下限。彩色打印需要更小的网点，而高质量打印通常需要2,400−3,000dpi。

在不同媒体上输出图像时，所需的分辨率往往不同。例如，在屏幕上显示图像时，每英寸的行数通常对应于每英寸的网格数。在报纸上显示图像时，通常使用至少 85 行/英寸的分辨率；对于杂志或书籍，通常使用每英寸至少 133 行或 175 行的分辨率。

CS代写|图像处理作业代写Image Processing代考请认准statistics-lab™

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。

金融工程代写

金融工程是使用数学技术来解决金融问题。金融工程使用计算机科学、统计学、经济学和应用数学领域的工具和知识来解决当前的金融问题，以及设计新的和创新的金融产品。

非参数统计代写

非参数统计指的是一种统计方法，其中不假设数据来自于由少数参数决定的规定模型；这种模型的例子包括正态分布模型和线性回归模型。

广义线性模型代考

广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

术语广义线性模型（GLM）通常是指给定连续和/或分类预测因素的连续响应变量的常规线性回归模型。它包括多元线性回归，以及方差分析和方差分析（仅含固定效应）。

有限元方法代写

有限元方法（FEM）是一种流行的方法，用于数值解决工程和数学建模中出现的微分方程。典型的问题领域包括结构分析、传热、流体流动、质量运输和电磁势等传统领域。

有限元是一种通用的数值方法，用于解决两个或三个空间变量的偏微分方程（即一些边界值问题）。为了解决一个问题，有限元将一个大系统细分为更小、更简单的部分，称为有限元。这是通过在空间维度上的特定空间离散化来实现的，它是通过构建对象的网格来实现的：用于求解的数值域，它有有限数量的点。边界值问题的有限元方法表述最终导致一个代数方程组。该方法在域上对未知函数进行逼近。[1] 然后将模拟这些有限元的简单方程组合成一个更大的方程系统，以模拟整个问题。然后，有限元通过变化微积分使相关的误差函数最小化来逼近一个解决方案。

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

随机分析代写

随机微积分是数学的一个分支，对随机过程进行操作。它允许为随机过程的积分定义一个关于随机过程的一致的积分理论。这个领域是由日本数学家伊藤清在第二次世界大战期间创建并开始的。

时间序列分析代写

随机过程，是依赖于参数的一组随机变量的全体，参数通常是时间。随机变量是随机现象的数量表现，其时间序列是一组按照时间发生先后顺序进行排列的数据点序列。通常一组时间序列的时间间隔为一恒定值（如1秒，5分钟，12小时，7天，1年），因此时间序列可以作为离散时间数据进行分析处理。研究时间序列数据的意义在于现实中，往往需要研究某个事物其随时间发展变化的规律。这就需要通过研究该事物过去发展的历史记录，以得到其自身发展的规律。

回归分析代写

多元回归分析渐进（Multiple Regression Analysis Asymptotics）属于计量经济学领域，主要是一种数学上的统计分析方法，可以分析复杂情况下各影响因素的数学关系，在自然科学、社会和经济学等多个领域内应用广泛。

MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习和应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

CS代写|图像处理作业代写Image Processing代考|ELE454

Posted on 2022年10月19日2022年10月19日 by statistics-lab

如果你也在怎样代写图像处理Image Processing这个学科遇到相关的难题，请随时右上角联系我们的24/7代写客服。

图像处理是使用数字计算机通过一种算法来处理数字图像。

我们提供的图像处理Image Processing及其相关学科的代写，服务范围广, 其中包括但不限于:

Statistical Inference 统计推断
Statistical Computing 统计计算
Advanced Probability Theory 高等楖率论
Advanced Mathematical Statistics 高等数理统计学
(Generalized) Linear Models 广义线性模型
Statistical Machine Learning 统计机器学习
Longitudinal Data Analysis 纵向数据分析
Foundations of Data Science 数据科学基础

CS代写|图像处理作业代写Image Processing代考|Images and Pixels

The objective world is three-dimensional (3-D) in space, but the image obtained from the objective scene is generally two-dimensional (2-D). An image can be represented by a 2-D array $f(x, y)$, where $x$ and $y$ represent the position of a coordinate point in the 2-D space $X Y$, and $f$ represents the image value of a property $F$ at a certain point $(x, y)$. For example, $f$ in a grayscale image represents a gray value, which often corresponds to the observed brightness of an objective scene. Text images are often binary images, and there are only two values for $f$, corresponding to text and blank space, respectively. The image at the point $(x, y)$ can also have multiple properties at the same time. In this case, it can be represented by a vector $f$. For example, a color image has three values of red, green, and blue at each image point, which can be recorded as $\left[f_r(x, y), f_g(x, y), f_b(x, y)\right]$. It needs to be pointed out that people always use images according to the different properties at different positions in the image.

An image can represent the spatial distribution of radiant energy. This distribution can be a function of five variables $T(x, y, z, t, \lambda)$, where $x, y$, and $z$ are spatial variables,

and $t$ represents time variables, $\lambda$ is wavelength (corresponding to the spectral variable). For example, a red object reflects light with a wavelength of $0.57-0.78 \mu \mathrm{m}$ and absorbs almost all energy of other wavelengths; a green object reflects light with a wavelength of $0.48-0.57 \mu \mathrm{m}$; a blue object reflects light with a wavelength of $0.40-0.48 \mu \mathrm{m}$. Ultraviolet (color) objects reflect light with a wavelength of $0.25-0.40 \mu \mathrm{m}$, and infrared (color) objects reflect light with a wavelength of $0.78-1.5 \mu \mathrm{m}$. Together, they cover a wavelength range of $0.25-1.5 \mu \mathrm{m}$. Since the actual image is finite in time and space, $T(x, y, z, t, \lambda)$ is a 5-D finite function.

The images acquired in the early years are mostly continuous (analog), that is, the values of $f, x$, and $y$ can be any real numbers. With the invention of the computer and the development of electronic equipment, the acquired images are all discrete (digital) and can be processed directly by the computer. Someone once used $I(r, c)$ to represent a digital image, where the values of $I, r$, and $c$ are all integers. Here $I$ represents the discretized $f ;(r, c)$ represents the discretized $(x, y)$, where $r$ represents the image row, and $c$ represents the image column. The discussion in this book is related to digital images. Images or $f(x, y)$ are used to represent digital images without causing confusion. Unless otherwise specified, $f, x$, and $y$ are all taken their values in the integer set.

In the early days, the term “picture” was generally used to refer to images. With the development of digital technology, the term “image” is now used to represent a discretized “image” because “computers store numerical images of a picture or scene” (Zhang 1996). Each basic unit in an image is called an image element, and in the early days, when the “picture” was used to represent an image, it was called a pixel. For 2-D images, “pel” has also been used to refer to the basic unit. If one collects a series of 2-D images or uses some special equipment, one can also get 3-D images. For 3-D images, voxel is often used to represent the basic unit. Someone has also suggested to use “imel” to represent various image units.

CS代写|图像处理作业代写Image Processing代考|Spatial Resolution and Amplitude Resolution

From the above introduction and discussion of image representation and display, it can be known that the content of a 2-D grayscale image is determined by the number of pixels (the number of rows of the image multiplied by the number of columns of the image) and by the number of gray levels for each pixel. The former determines the spatial resolution of the image, while the latter determines the amplitude resolution of the image. From the perspective of image acquisition, the acquisition of images is to record the spatial distribution of the light reflection intensity of the scene within a certain field of view. The accuracy in the spatial field of view here corresponds to the spatial resolution of the image, and the accuracy in the intensity range corresponds to the amplitude resolution of the image. The former corresponds to the number of digitized spatial sampling points while the latter corresponds to the quantization levels of the sampling point value (for grayscale images, it refers to gray levels; for depth images, it refers to depth levels). They are all important performance indicators of image acquisition devices.

The spatial resolution and amplitude resolution of the image are determined by sampling and quantization, respectively. Taking a typical CCD camera as an example, the spatial resolution of the image is mainly determined by the size and arrangement of the photoelectric sensing units in the image acquisition matrix in the camera, and the amplitude resolution of the grayscale image is mainly determined by the number of stages in the quantization of the electrical signal intensity. As shown in Figure 1.3, the signal radiated from the photoreceptive unit in the image acquisition matrix is sampled in space and quantized in intensity.

The sampling process can be seen as dividing the image plane into regular grids. The position of each grid is determined by a pair of Cartesian coordinates $(x, y)$, where $x$ and $y$ are integers. Let $f(\cdot)$ be a function that assigns gray values to the grid point $(x, y)$, where $f$ is an integer in $F$, then $f(x, y)$ is a digital image, and this assignment process is a quantization process.

From the perspective of computer processing of images, an image must be discretized in space and gray level before it can be processed by the computer. The discretization of spatial coordinates is called spatial sampling (abbreviated as sampling), which determines the spatial resolution of the image; the discretization of gray values is called grayscale quantization (abbreviated as quantization), which determines the amplitude resolution of the image.

图像处理代考

CS代写|图像处理作业代写Image Processing代考|Images and Pixels

客观世界在空间上是三维的（3-D），但从客观场景得到的图像一般是二维的（2-D）。图像可以用二维数组表示F(X,是)，在哪里X和是表示坐标点在二维空间中的位置X是，和F表示属性的图像值F在某一点(X,是). 例如，F在灰度图像中表示一个灰度值，它通常对应于观察到的客观场景的亮度。文本图像通常是二值图像，只有两个值F，分别对应文本和空格。该点的图像(X,是)也可以同时拥有多个属性。在这种情况下，它可以用一个向量来表示F. 例如，一幅彩色图像在每个图像点上具有红、绿、蓝三个值，可以记为[Fr(X,是),FG(X,是),Fb(X,是)]. 需要指出的是，人们总是根据图像中不同位置的不同属性来使用图像。

图像可以表示辐射能量的空间分布。该分布可以是五个变量的函数吨(X,是,和,吨,l)，在哪里X,是，和和是空间变量，

和吨表示时间变量，l是波长（对应于光谱变量）。例如，红色物体反射波长为0.57−0.78米米并吸收几乎所有其他波长的能量；绿色物体反射波长为0.48−0.57米米; 蓝色物体反射波长为0.40−0.48米米. 紫外线（彩色）物体反射波长为0.25−0.40米米, 红外（彩色）物体反射波长为0.78−1.5米米. 它们共同覆盖的波长范围为0.25−1.5米米. 由于实际图像在时间和空间上是有限的，吨(X,是,和,吨,l)是一个 5-D 有限函数。

早年获取的图像大多是连续的（模拟的），即F,X，和是可以是任何实数。随着计算机的发明和电子设备的发展，所获取的图像都是离散的（数字的），可以直接由计算机处理。曾经有人用过我(r,C)表示数字图像，其中的值我,r，和C都是整数。这里我表示离散的F;(r,C)表示离散的(X,是)，在哪里r表示图像行，并且C表示图像列。本书中的讨论与数字图像有关。图像或F(X,是)用于表示数字图像而不会引起混淆。除非另有规定，F,X，和是都在整数集中取它们的值。

在早期，“图片”一词通常用于指代图像。随着数字技术的发展，“图像”一词现在被用来表示离散化的“图像”，因为“计算机存储了图片或场景的数字图像”（Zhang 1996）。图像中的每个基本单元称为图像元素，在早期，当“图片”用于表示图像时，称为像素。对于二维图像，“pel”也被用来指代基本单位。如果收集一系列 2-D 图像或使用一些特殊设备，也可以得到 3-D 图像。对于 3D 图像，通常使用体素来表示基本单位。也有人建议使用“imel”来表示各种图像单元。

CS代写|图像处理作业代写Image Processing代考|Spatial Resolution and Amplitude Resolution

从以上对图像表示和显示的介绍和讨论可知，一张二维灰度图像的内容是由像素数（图像的行数乘以图像的列数）决定的) 和每个像素的灰度级数。前者决定了图像的空间分辨率，而后者决定了图像的幅度分辨率。从图像采集的角度来看，图像的采集就是记录一定视野内场景的光反射强度的空间分布。这里的空间视场精度对应于图像的空间分辨率，强度范围内的精度对应于图像的幅度分辨率。前者对应的是数字化的空间采样点的个数，后者对应的是采样点值的量化级别（对于灰度图像，它是指灰度级；对于深度图像，它是指深度级别）。它们都是图像采集设备的重要性能指标。

图像的空间分辨率和幅度分辨率分别由采样和量化决定。以典型的CCD相机为例，图像的空间分辨率主要由相机内图像采集矩阵中光电传感单元的大小和排列方式决定，而灰度图像的幅值分辨率主要由电信号强度量化的阶段数。如图 1.3 所示，从图像采集矩阵中的感光单元辐射的信号在空间中进行采样并在强度上进行量化。

采样过程可以看作是将图像平面划分为规则的网格。每个网格的位置由一对笛卡尔坐标确定(X,是)，在哪里X和是是整数。让F(⋅)是一个将灰度值分配给网格点的函数(X,是)，在哪里F是一个整数F，然后F(X,是)是数字图像，这个赋值过程是一个量化过程。

从计算机处理图像的角度来看，图像必须在空间和灰度上进行离散化，才能被计算机处理。空间坐标的离散化称为空间采样（简称采样），它决定了图像的空间分辨率；灰度值的离散化称为灰度量化（简称量化），它决定了图像的幅值分辨率。

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。

金融工程代写

非参数统计代写

非参数统计指的是一种统计方法，其中不假设数据来自于由少数参数决定的规定模型；这种模型的例子包括正态分布模型和线性回归模型。

广义线性模型代考

广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

有限元方法代写

随机分析代写

时间序列分析代写

回归分析代写

MATLAB代写

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

CS代写|数据可视化代写Data visualization代考|EDS240

Posted on 2022年10月19日2022年10月19日 by statistics-lab

如果你也在怎样代写数据可视化Data visualization这个学科遇到相关的难题，请随时右上角联系我们的24/7代写客服。

数据可视化是将信息转化为视觉背景的做法，如地图或图表，使数据更容易被人脑理解并从中获得洞察力。数据可视化的主要目标是使其更容易在大型数据集中识别模式、趋势和异常值。

statistics-lab™ 为您的留学生涯保驾护航在代写数据可视化Data visualization方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写数据可视化Data visualization代写方面经验极为丰富，各种代写数据可视化Data visualization相关的作业也就用不着说。

我们提供的数据可视化Data visualization及其相关学科的代写，服务范围广, 其中包括但不限于:

Statistical Inference 统计推断
Statistical Computing 统计计算
Advanced Probability Theory 高等概率论
Advanced Mathematical Statistics 高等数理统计学
(Generalized) Linear Models 广义线性模型
Statistical Machine Learning 统计机器学习
Longitudinal Data Analysis 纵向数据分析
Foundations of Data Science 数据科学基础

CS代写|数据可视化代写Data visualization代考|Human Resource Management

Human resource management (HRM) is the part of an organization that focuses on an organization’s recruitment, training, and retention of employees. With the increased use of analytics in business, HRM has become much more data-driven. Indeed, HRM is sometimes now referred to as “people analytics.” HRM professionals use data and analytical models to form high-performing teams, monitor productivity and employee performance, and ensure diversity of the workforce. Data visualization is an important component of HRM, as HRM professionals use data dashboards to monitor relevant data supporting their goal of having a high-performing workforce.
A key interest of HRM professionals is employee churn, or turnover in an organization’s workforce. When employees leave and others are hired, there is often a loss of productivity as positions go unfilled. Also, new employees typically have a training period and then must gain experience, which means employees will not be fully productive at the beginning of their tenure with the company. Figure $1.8$, a stacked column chart, is an example of a visual display of employee turnover. It shows gains and losses of employees by month. A stacked column chart is a column chart that shows part-to-whole comparisons, either over time or across categories. Different colors or shades of color are used to denote the different parts of the whole within a column. In Figure 1.8, gains in employees (new hires) are represented by positive numbers in darker blue and losses (people leaving the company) are presented as negative numbers and lighter blue bars. We see that January and July-October are the months during which the greatest numbers of employees left the company, and the months with the highest numbers of new hires are April through June.Visualizations like Figure $1.8$ can be helpful in better understanding and managing workforce fluctuations.

CS代写|数据可视化代写Data visualization代考|Marketing

Marketing is one of the most popular application areas of analytics. Analytics lis used for optimal pricing, markdown pricing for seasonal goods, and optimal allocation of marketing budget. Sentiment analysis using text data such as tweets, social networks to determine influence, and website analytics for understanding website traffic and sales, are just a few examples of how data visualization can be used to support more effective marketing.
Let us consider a software company’s website effectiveness. Figure $1.9$ shows a funnel chart of the conversion of website visitors to subscribers and then to renewal customers. A funnel chart is a chart that shows the progression of a numerical variable for various categories from larger to smaller values. In Figure 1.9, at the top of the funnel, we track $100 \%$ of the first-time visitors to the website over some period of time, for example, a six-month period. The funnel chart shows that of those original visitors, $74 \%$ return to the website one or more times after their initial visit. Sixty-one percent of the first-time visitors downloaded a 30-day trial version of the software, $47 \%$ eventually contacted support services, $28 \%$ purchased a one-year subscription to the software, and $17 \%$ eventually renewed their subscription. This type of funnel chart can be used to compare the conversion effectiveness of different website configurations, the use of bots, or changes in support services.

数据可视化代考

CS代写|数据可视化代写Data visualization代考|Human Resource Management

人力资源管理 (HRM) 是组织的一部分，专注于组织的招聘、培训和留住员工。随着在业务中越来越多地使用分析，HRM 变得更加数据驱动。事实上，人力资源管理现在有时被称为“人员分析”。人力资源管理专业人员使用数据和分析模型来组建高绩效团队，监控生产力和员工绩效，并确保员工队伍的多样性。数据可视化是 HRM 的重要组成部分，因为 HRM 专业人员使用数据仪表板来监控相关数据，以支持他们拥有高绩效员工队伍的目标。
人力资源管理专业人士的一个主要兴趣是员工流失或组织劳动力的流动。当员工离开并雇用其他人时，由于职位空缺，通常会降低生产力。此外，新员工通常有一个培训期，然后必须获得经验，这意味着员工在公司任职之初不会充分发挥生产力。数字1.8，堆积柱形图，是员工流失率的可视化显示示例。它按月显示员工的得失。堆积柱形图是一种柱形图，显示了部分与整体的比较，无论是随着时间的推移还是跨类别的比较。不同的颜色或颜色深浅用于表示列内整体的不同部分。在图 1.8 中，员工（新员工）的收益用深蓝色的正数表示，损失（离开公司的人）用负数和浅蓝色条表示。我们看到，1 月和 7 月至 10 月是员工离职人数最多的月份，而新员工人数最多的月份是 4 月至 6 月。1.8有助于更好地理解和管理劳动力波动。

CS代写|数据可视化代写Data visualization代考|Marketing

营销是分析最流行的应用领域之一。分析列表用于优化定价、季节性商品的降价定价以及营销预算的优化分配。使用文本数据（例如推文、社交网络来确定影响力）和网站分析来了解网站流量和销售的情感分析只是数据可视化如何用于支持更有效营销的几个例子。
让我们考虑一个软件公司的网站有效性。数字1.9显示网站访问者转换为订阅者，然后再转换为续订客户的漏斗图。漏斗图是显示各种类别的数值变量从较大值到较小值的进展的图表。在图 1.9 中，在漏斗的顶部，我们跟踪100%在某个时间段内（例如，六个月的时间段）首次访问该网站的访问者。漏斗图显示了那些原始访问者，74%在初次访问后返回网站一次或多次。61% 的首次访问者下载了该软件的 30 天试用版，47%最终联系了支持服务，28%购买了一年的软件订阅，并且17%最终续订了他们的订阅。这种漏斗图可用于比较不同网站配置、机器人使用或支持服务变化的转化效果。

CS代写|数据可视化代写Data visualization代考请认准statistics-lab™

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。

金融工程代写

非参数统计代写

非参数统计指的是一种统计方法，其中不假设数据来自于由少数参数决定的规定模型；这种模型的例子包括正态分布模型和线性回归模型。

广义线性模型代考

广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

有限元方法代写

随机分析代写

时间序列分析代写

回归分析代写

MATLAB代写

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

CS代写|数据可视化代写Data visualization代考|INF552

Posted on 2022年10月19日2022年10月19日 by statistics-lab

如果你也在怎样代写数据可视化Data visualization这个学科遇到相关的难题，请随时右上角联系我们的24/7代写客服。

我们提供的数据可视化Data visualization及其相关学科的代写，服务范围广, 其中包括但不限于:

Statistical Inference 统计推断
Statistical Computing 统计计算
Advanced Probability Theory 高等概率论
Advanced Mathematical Statistics 高等数理统计学
(Generalized) Linear Models 广义线性模型
Statistical Machine Learning 统计机器学习
Longitudinal Data Analysis 纵向数据分析
Foundations of Data Science 数据科学基础

CS代写|数据可视化代写Data visualization代考|Big Data

There is no universally accepted definition of big data. However, probably the most general definition of big data is any set of data that is too large or too complex to be handled by standard data-processing techniques using a typical desktop computer. People refer to the four $\mathrm{Vs}$ of big data:

volume-the amount of data generated
velocity-the speed at which the data are generated
variety-the diversity in types and structures of data generated
veracity-the reliability of the data generated
Volume and velocity can pose a challenge for processing analytics, including data visualization. Special data management software such as Hadoop and higher capacity hardware (increased server or cloud computing) may be required. The variety of the data is handled by converting video, voice, and text data to numerical data, to which we can then apply standard data visualization techniques.
In summary, the type of data you have will influence the type of graph you should use to convey your message. The zoo attendance data in Figure $1.1$ are time series data. We used a column chart in Figure $1.1$ because the numbers are the total attendance for each month, and we wanted to compare the attendance by month. The height of the columns allows us to easily compare attendance by month. Contrast Figure $1.1$ with Figure 1.4, which is also time series data. Here we have the value of the Dow Jones Index. These data are a snapshot of the current value of the DJI on the first trading day of each month. They provide what is essentially a time path of the value, and so we use a line graph to emphasize the continuity of time.

CS代写|数据可视化代写Data visualization代考|Data Visualization in Practice

Data visualization is used to explore and explain data and to guide decision making in all areas of business and science. Even the most analytically advanced companies such as Google, Uber, and Amazon rely heavily on data visualization. Consumer goods giant Procter \& Gamble (P\&G), the maker of household brands such as Tide, Pampers, Crest, and Swiffer, has invested heavily in analytics, including data visualization. P\&G has built what it calls the Business Sphere ${ }^{\mathrm{TM}}$ in more than 50 of its sites around the world. The Business Sphere is a conference room with technology for displaying data visualizations on its walls. The Business Sphere displays data and information P\&G executives and managers can use to make better-informed decisions. Let us briefly discuss some ways in which the functional areas of business, engineering, science, and sports use data visualization.

Accounting is a data-driven profession. Accountants prepare financial statements and examine financial statements for accuracy and conformance to legal regulations and best practices, including reporting required for tax purposes. Data visualization is a part of every accountant’s tool kit. Data visualization is used to detect outliers that could be an indication of a data error or fraud. As an example of data visualization in accounting, let us consider Benford’s Law.
Benfords Law, also known as the First-Digit Law, gives the expected probability that the first digit of a reported number takes on the values one through nine, based on many real-life numerical data sets such as company expense accounts. A column chart displaying Benford’s Law is shown in Figure 1.5. We have rounded the probabilities to four digits. We see, for example, that the probability of the first digit being a 1 is $0.3010$. The probability of the first digit being a 2 is $0.1761$, and so forth.

Benford’s Law can be used to detect fraud. If the first digits of numbers in a data set do not conform to Bedford’s Law, then further investigation of fraud may be warranted. Consider the accounts payable (money owed the company) for Tucker Software. Figure $1.6$ is a clustered column chart (also known as a side-by-side column chart). A clustered column chart is a column chart that shows multiple variables of interest on the same chart, with the different variables usually denoted by different colors or shades of a color. In Figure 1.6, the two variables are Benford’s Law probability and the first digit data for a random sample of 500 of Tucker’s accounts payable entries. The frequency of occurrence in the data is used to estimate the probability of the first digit for all of Tucker’s accounts payable entries. It appears that there are an inordinate number of first digits of 5 and 9 and a lower than expected number of first digits of 1 . These might warrant further investigation by Tucker’s auditors.

数据可视化代考

CS代写|数据可视化代写Data visualization代考|Big Data

大数据没有普遍接受的定义。然而，大数据最一般的定义可能是任何太大或太复杂而无法通过使用典型台式计算机的标准数据处理技术处理的数据集。人们指的是四在s大数据：

volume——产生的数据量
速度——生成数据的速度
多样性——生成的数据类型和结构的多样性
准确性——生成的数据的可靠性
数量和速度可能对处理分析（包括数据可视化）提出挑战。可能需要特殊的数据管理软件，例如 Hadoop 和更高容量的硬件（增加的服务器或云计算）。通过将视频、语音和文本数据转换为数字数据来处理各种数据，然后我们可以对其应用标准数据可视化技术。
总之，您拥有的数据类型将影响您应该用来传达信息的图表类型。动物园出勤数据如图1.1是时间序列数据。我们在图中使用了柱形图1.1因为这些数字是每个月的总出勤率，我们想按月比较出勤率。列的高度使我们可以轻松地按月比较出勤率。对比图1.1图 1.4 也是时间序列数据。这里我们有道琼斯指数的价值。这些数据是每个月第一个交易日 DJI 当前价值的快照。它们提供了本质上是价值的时间路径，因此我们使用折线图来强调时间的连续性。

CS代写|数据可视化代写Data visualization代考|Data Visualization in Practice

数据可视化用于探索和解释数据，并指导所有商业和科学领域的决策。即使是分析能力最先进的公司，如谷歌、优步和亚马逊，也严重依赖数据可视化。消费品巨头宝洁 (P\&G) 是 Tide、帮宝适、佳洁士和 Swiffer 等家居品牌的制造商，它在数据可视化等分析方面投入了大量资金。宝洁建立了它所谓的商业领域吨米在其全球 50 多个站点中。Business Sphere 是一个会议室，其墙壁上显示数据可视化技术。业务领域显示宝洁高管和经理可以用来做出更明智决策的数据和信息。让我们简要讨论商业、工程、科学和体育等职能领域使用数据可视化的一些方式。

会计是一个数据驱动的职业。会计师准备财务报表并检查财务报表的准确性和是否符合法律法规和最佳实践，包括出于税收目的所需的报告。数据可视化是每个会计师工具包的一部分。数据可视化用于检测可能表明数据错误或欺诈的异常值。作为会计中数据可视化的一个例子，让我们考虑一下本福德定律。
本福德定律，也称为第一位定律，根据许多现实生活中的数字数据集（例如公司费用账户）给出了报告数字的第一位数字取值 1 到 9 的预期概率。显示本福德定律的柱形图如图 1.5 所示。我们将概率四舍五入为四位数。例如，我们看到第一个数字是 1 的概率是0.3010. 第一个数字是 2 的概率是0.1761，等等。

本福德定律可用于检测欺诈。如果数据集中数字的前几位不符合贝德福德定律，则可能需要对欺诈行为进行进一步调查。考虑一下 Tucker Software 的应付账款（欠公司的钱）。数字1.6是聚集柱形图（也称为并排柱形图）。聚集柱形图是在同一个图表上显示多个感兴趣的变量的柱形图，不同的变量通常用不同的颜色或颜色的深浅来表示。在图 1.6 中，这两个变量是 Benford 定律概率和随机样本的 500 个 Tucker 应付账款分录的第一位数据。数据中出现的频率用于估计 Tucker 的所有应付账款条目的第一位数字的概率。似乎有过多的第一位数字 5 和 9 以及低于预期的第一位数字 1 。这些可能需要 Tucker 的审计师进一步调查。

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。

金融工程代写

非参数统计代写

非参数统计指的是一种统计方法，其中不假设数据来自于由少数参数决定的规定模型；这种模型的例子包括正态分布模型和线性回归模型。

广义线性模型代考

广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

有限元方法代写

随机分析代写

时间序列分析代写

回归分析代写

MATLAB代写

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

CS代写|数据可视化代写Data visualization代考|BINF7003

Posted on 2022年10月19日2022年10月19日 by statistics-lab

如果你也在怎样代写数据可视化Data visualization这个学科遇到相关的难题，请随时右上角联系我们的24/7代写客服。

我们提供的数据可视化Data visualization及其相关学科的代写，服务范围广, 其中包括但不限于:

Statistical Inference 统计推断
Statistical Computing 统计计算
Advanced Probability Theory 高等概率论
Advanced Mathematical Statistics 高等数理统计学
(Generalized) Linear Models 广义线性模型
Statistical Machine Learning 统计机器学习
Longitudinal Data Analysis 纵向数据分析
Foundations of Data Science 数据科学基础

CS代写|数据可视化代写Data visualization代考|BINF7003

CS代写|数据可视化代写Data visualization代考|Data Visualization for Exploration

Data visualization is a powerful tool for exploring data to more easily identify patterns, recognize anomalies or irregularities in the data, and better understand the relationships between variables. Our ability to spot these types of characteristics of data is much stronger and quicker when we look at a visual display of the data rather than a simple listing.
As an example of data visualization for exploration, let us consider the zoo attendance data shown in Table $1.1$ and Figure 1.1. These data on monthly attendance to a zoo can be found in the file Zoo. Comparing Table $1.1$ and Figure 1.1, observe that the pattern in the data is more detectable in the column chart of Figure $1.1$ than in a table of numbers. A column chart shows numerical data by the height of the column for a variety of categories or time periods. In the case of Figure 1.1, the time periods are the different months of the year.

Our intuition and experience tells us that we would expect zoo attendance to be highest in the summer months when many school-aged children are out of school for summer break. Figure $1.1$ confirms this, as the attendance at the zoo is highest in the summer months of June, July, and August. Furthermore, we see that attendance increases gradually each month from February through May as the average temperature increases, and attendance gradually decreases each month from September through November as the average temperature decreases. But why does the zoo attendance in December and January not follow these patterns? It turns out that the zoo has an event known as the “Festival of Lights” that runs from the end of November through early January. Children are out of school during the last half of December and early January for the holiday season, and this leads to increased attendance in the evenings at the zoo despite the colder winter temperatures.
Visual data exploration is an important part of descriptive analytics. Data visualization can also be used directly to monitor key performance metrics, that is, measure how an organization is performing relative to its goals. A data dashboard is a data visualization tool that gives multiple outputs and may update in real time. Just as the dashboard in your car measures the speed, engine temperature, and other important performance data as you drive, corporate data dashboards measure performance metrics such as sales, inventory levels, and service levels relative to the goals set by the company. These data dashboards alert management when performances deviate from goals so that corrective actions can be taken.
Visual data exploration is also critical for ensuring that model assumptions hold in predictive and prescriptive analytics. Understanding the data before using that data in modeling builds trust and can be important in determining and explaining which type of model is appropriate.

CS代写|数据可视化代写Data visualization代考|Data Visualization for Explanation

Data visualization is also important for explaining relationships found in data and for explaining the results of predictive and prescriptive models. More generally, data visualization is helpful in communicating with your audience and ensuring that your audience understands and focuses on your intended message.

Let us consider the article, “Check Out the Culture Before a New Job,” which appeared in The Wall Street Journal. ${ }^3$ The article discusses the importance of finding a good cultural fit when seeking a new job. Difficulty in understanding a corporate culture or misalignment with that culture can lead to job dissatisfaction. Figure $1.3$ is a re-creation of a bar chart that appeared in this article. A bar chart shows a summary of categorical data using the length of horizontal bars to display the magnitude of a quantitative variable.

The chart shown in Figure $1.3$ shows the percentage of the 10,002 survey respondents who listed a factor as the most important in seeking a job. Notice that our attention is drawn to the dark blue bar, which is “Company culture” (the focus of the article). We immediately see that only “Salary and bonus” is more frequently cited than “Company culture.” When you first glance at the chart, the message that is communicated is that corporate culture is the second most important factor cited by job seekers. And as a reader, based on that message, you then decide whether the article is worth reading.

数据可视化代考

CS代写|数据可视化代写Data visualization代考|Data Visualization for Exploration

数据可视化是一种强大的工具，用于探索数据以更轻松地识别模式、识别数据中的异常或不规则性以及更好地理解变量之间的关系。当我们查看数据的可视化显示而不是简单的列表时，我们发现这些类型的数据特征的能力要强得多、更快。
作为探索的数据可视化示例，让我们考虑表中所示的动物园出勤数据1.1图 1.1。这些关于动物园每月出勤人数的数据可以在文件 Zoo 中找到。比较表1.1和图1.1，观察数据中的模式在图的柱形图中更容易察觉1.1比在数字表中。柱形图按柱高显示各种类别或时间段的数值数据。在图 1.1 的情况下，时间段是一年中的不同月份。

我们的直觉和经验告诉我们，当许多学龄儿童放暑假时，我们预计动物园的出勤率会在夏季最高。数字1.1证实了这一点，因为动物园的出勤率在 6 月、7 月和 8 月的夏季月份最高。此外，我们看到从 2 月到 5 月，随着平均温度的升高，每个月的出勤率逐渐增加，而从 9 月到 11 月，随着平均温度的降低，出勤率每个月都在逐渐减少。但为什么 12 月和 1 月的动物园出勤率不遵循这些模式？原来，动物园有一个被称为“灯光节”的活动，从 11 月底一直持续到 1 月初。孩子们在 12 月下半月和 1 月初的假期期间不上学，这导致尽管冬季气温较低，但动物园晚上的出勤人数有所增加。
可视化数据探索是描述性分析的重要组成部分。数据可视化还可以直接用于监控关键绩效指标，即衡量组织相对于其目标的绩效。数据仪表板是一种数据可视化工具，可提供多种输出并可实时更新。就像您汽车中的仪表板在您驾驶时测量速度、发动机温度和其他重要性能数据一样，公司数据仪表板测量与公司设定的目标相关的销售、库存水平和服务水平等绩效指标。当绩效偏离目标时，这些数据仪表板会提醒管理层，以便采取纠正措施。
可视化数据探索对于确保模型假设适用于预测性和规范性分析也至关重要。在建模中使用数据之前了解数据可以建立信任，并且对于确定和解释哪种模型是合适的很重要。

CS代写|数据可视化代写Data visualization代考|Data Visualization for Explanation

数据可视化对于解释数据中发现的关系以及解释预测和规范模型的结果也很重要。更一般地说，数据可视化有助于与您的受众交流并确保您的受众理解并关注您的预期信息。

让我们考虑一下发表在《华尔街日报》上的文章“在新工作之前检查文化”。3这篇文章讨论了在寻找新工作时找到良好文化契合度的重要性。难以理解企业文化或与该文化不一致会导致工作不满。数字1.3是本文中出现的条形图的重新创建。条形图显示分类数据的摘要，使用水平条的长度来显示定量变量的大小。

如图所示的图表1.3显示了 10,002 名受访者中将某个因素列为求职最重要因素的百分比。请注意，我们的注意力被吸引到深蓝色条上，即“公司文化”（本文的重点）。我们立即看到只有“薪水和奖金”比“公司文化”更频繁地被引用。当您第一眼看到图表时，传达的信息是企业文化是求职者引用的第二个最重要的因素。作为读者，根据该信息，您可以决定这篇文章是否值得一读。

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。

金融工程代写

非参数统计代写

非参数统计指的是一种统计方法，其中不假设数据来自于由少数参数决定的规定模型；这种模型的例子包括正态分布模型和线性回归模型。

广义线性模型代考

广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

有限元方法代写

随机分析代写

时间序列分析代写

回归分析代写

MATLAB代写

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

cs代写|复杂网络代写complex network代考|TSKS33

Posted on 2022年9月1日2022年9月1日 by statistics-lab

如果你也在怎样代写复杂网络complex network这个学科遇到相关的难题，请随时右上角联系我们的24/7代写客服。

在网络理论的背景下，复杂网络是具有非微观拓扑特征的图（网络）这些特征在格子或随机图等简单网络中不出现，但在代表真实系统的网络中经常出现。

statistics-lab™ 为您的留学生涯保驾护航在代写复杂网络complex network方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写复杂网络complex network代写方面经验极为丰富，各种代写复杂网络complex network相关的作业也就用不着说。

我们提供的复杂网络complex network及其相关学科的代写，服务范围广, 其中包括但不限于:

Statistical Inference 统计推断
Statistical Computing 统计计算
Advanced Probability Theory 高等概率论
Advanced Mathematical Statistics 高等数理统计学
(Generalized) Linear Models 广义线性模型
Statistical Machine Learning 统计机器学习
Longitudinal Data Analysis 纵向数据分析
Foundations of Data Science 数据科学基础

cs代写|复杂网络代写complex network代考|EXTENSIONS AND APPLICATIONS OF CNSS

In the above sections, we have surveyed some recent developments in the analysis and synthesis of CNSs with switching topologies, mainly focusing on the synchronization and consensus behaviors and comparison to complex networks and MASs’ scenarios. The above survey is by no means complete. However, it depicts the whole general framework of coordination control for CNSs with dynamic communication networks and lays the fundamental basis for other exciting and yet critical issues concerning CNSs with switching topologies. These extensions still deserve further study, although a variety of efficient tools have been successfully developed to solve various challenging problems in those active research fields. Next, we elaborate on several state-of-the-art extensions and applications of CNSs with dynamic topologies.

Resilience analysis and control of complex cyber-physical networks. Most of the units in various network infrastructures are cyber-physical systems in the Internet of Things era. One of the essential and significant features of the cyber-physical system is integrating and interacting with its physical and cyber layers. As a new generation of CNS, the complex cyber-physical network has received drastic attention in recent years. Specifically, the CNSs’ paradigm provides an excellent way to model various large-scale crucial infrastructure systems, such as power grid systems, transportation systems, water supply networks, and many others [4]. These systems all capture the basic features that large numbers of interconnected individuals through wired or wireless communication links, and many essential functions of these large-scale infrastructure systems fall under the purview of coordination of CNSs. Disruption of these critical networked infrastructures could be a real-world effect across an entire country and even further, significantly impacting public health and safety and leading to massive economic losses. The alarming historical events urgently remind us to seek solutions for maintaining certain functionality of CNSs against malicious cyberattacks (i.e., resilience or cybersecurity). It is critically essential to exploit security threats during the initial design and development phase.

cs代写|复杂网络代写complex network代考|ALGEBRAIC GRAPH THEORY

Suppose a CNS consists of $N$ nodes (agents) which interact with each other through a communication or sensing network or a combination of both. It is natural to model the interactions among the $N$ nodes (agents) by undirected or directed graphs. Without loss generality, the $N$ nodes can be labeled as node $1, \ldots, N$. Let $\mathcal{V}={1, \cdots, N}$ be the set of nodes. Then the directed graph is described by $(\mathcal{V}, \mathcal{E})$, where the set of edges $\mathcal{E} \subseteq \mathcal{V} \times \mathcal{V}$ represent the interactions among the $N$ nodes. For notational simplicity, the graph $(\mathcal{V}, \mathcal{E})$ is denoted by $\mathcal{G}$. The edge $(j, i) \in \mathcal{E}$ if and only if node $i$ can receive the information from node $j$. When $(j, i) \in \mathcal{E}$, node $j$ is said to be a neighbor of node $i$. Denote by $\mathcal{N}i$ the set of neighbors of node $i$. If there exists a sequence of distinct nodes $i_1, \ldots, i_m$ such that $\left(i, i_1\right),\left(i_1, i_2\right), \ldots,\left(i{m-1}, i_m\right),\left(i_m, k\right) \in \mathcal{E}$, then it is said that node $i$ has a directed path to node $k$, or node $k$ is reachable from node $i . \mathcal{G}$ is strongly connected if each node has at least one directed path to any other nodes. More generally, if there exists a node, called the root, which has at least one directed path to any other nodes, $\mathcal{G}$ is said to contain a directed spanning tree. Denote by $a_{i j}$ the weight of the edge $(j, i), i, j=1, \ldots, N$. It is assumed throughout this book that $a_{i j} \geq 0$, where $a_{i j}>0$ if and only if $(j, i) \in \mathcal{E}$, and $a_{i j}=0$, otherwise. In addition, it is assumed in this book that $a_{i i}=0$, that is, self-loop is forbidden. $\mathcal{G}$ is called an undirected graph if $(i, j) \in \mathcal{E}$ whenever $(j, i) \in \mathcal{E}$ and $a_{i j}=a_{j i}$. An undirected graph is connected if there exists at least one undirected path between each pair of distinct nodes. For undirected graphs, the existence of an undirected spanning tree is equivalent to being connected. However, for directed graphs, the existence of a directed spanning tree is a weaker condition than being strongly connected. Please see Figure $2.1$ for a directed graph which is not strongly connected but contains a directed spanning tree.

复杂网络代写

cs代写|复杂网络代写complex network代考|EXTENSIONS AND APPLICATIONS OF CNSS

在上述部分中，我们回顾了具有切换拓扑的 CNS 分析和综合的一些最新进展，主要关注同步和共识行为以及与复杂网络和 MAS 场景的比较。上述调查并不完整。然而，它描述了具有动态通信网络的 CNS 协调控制的整个一般框架，并为其他有关具有交换拓扑的 CNS 的令人兴奋但关键的问题奠定了基础。这些扩展仍然值得进一步研究，尽管已经成功开发了各种有效的工具来解决这些活跃研究领域中的各种具有挑战性的问题。接下来，我们详细阐述了具有动态拓扑的 CNS 的几个最先进的扩展和应用。

复杂网络物理网络的弹性分析和控制。各种网络基础设施中的大部分单元都是物联网时代的信息物理系统。网络物理系统的基本和重要特征之一是与其物理层和网络层集成和交互。作为新一代的CNS，复杂的信息物理网络近年来受到了广泛关注。具体来说，CNS 的范式为模拟各种大型关键基础设施系统（如电网系统、交通系统、供水网络等）提供了一种极好的方法 [4]。这些系统都捕捉到大量通过有线或无线通信链路相互连接的个体的基本特征，这些大型基础设施系统的许多基本功能都属于 CNS 协调的范围。这些关键网络基础设施的中断可能会对整个国家产生现实影响，甚至会进一步严重影响公共健康和安全，并导致巨大的经济损失。令人震惊的历史事件紧急提醒我们寻求解决方案，以维护 CNS 的某些功能以抵御恶意网络攻击（即弹性或网络安全）。在初始设计和开发阶段利用安全威胁至关重要。严重影响公共健康和安全，并导致巨大的经济损失。令人震惊的历史事件紧急提醒我们寻求解决方案，以维护 CNS 的某些功能以抵御恶意网络攻击（即弹性或网络安全）。在初始设计和开发阶段利用安全威胁至关重要。严重影响公共健康和安全，并导致巨大的经济损失。令人震惊的历史事件紧急提醒我们寻求解决方案，以维护 CNS 的某些功能以抵御恶意网络攻击（即弹性或网络安全）。在初始设计和开发阶段利用安全威胁至关重要。

cs代写|复杂网络代写complex network代考|ALGEBRAIC GRAPH THEORY

假设一个 CNS 由ñ通过通信或传感网络或两者的组合相互交互的节点（代理）。模型之间的相互作用是很自然的ñ通过无向或有向图的节点（代理）。不失一般性，ñ节点可以标记为节点1,…,ñ. 让在=1,⋯,ñ是节点的集合。然后有向图描述为(在,和), 其中边的集合和⊆在×在表示之间的相互作用ñ节点。为符号简单起见，该图(在,和)表示为G. 边缘(j,一世)∈和当且仅当节点一世可以从节点接收信息j. 什么时候(j,一世)∈和, 节点j被称为节点的邻居一世. 表示为ñ一世节点的邻居集合一世. 如果存在一系列不同的节点一世1,…,一世米这样(一世,一世1),(一世1,一世2),…,(一世米−1,一世米),(一世米,ķ)∈和，则称该节点一世有一个指向节点的路径ķ, 或节点ķ可从节点访问一世.G如果每个节点至少有一条到任何其他节点的有向路径，则它是强连接的。更一般地说，如果存在一个称为根的节点，它至少有一条到任何其他节点的有向路径，G据说包含有向生成树。表示为一个一世j边缘的重量(j,一世),一世,j=1,…,ñ. 本书通篇假定一个一世j≥0，在哪里一个一世j>0当且仅当(j,一世)∈和，和一个一世j=0，否则。此外，本书假设一个一世一世=0，即禁止自循环。G称为无向图，如果(一世,j)∈和每当(j,一世)∈和和一个一世j=一个j一世. 如果每对不同的节点之间至少存在一条无向路径，则无向图是连通的。对于无向图，无向生成树的存在相当于是连通的。然而，对于有向图，有向生成树的存在是比强连接更弱的条件。请看图2.1对于没有强连接但包含有向生成树的有向图。

cs代写|复杂网络代写complex network代考请认准statistics-lab™

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。

金融工程代写

非参数统计代写

非参数统计指的是一种统计方法，其中不假设数据来自于由少数参数决定的规定模型；这种模型的例子包括正态分布模型和线性回归模型。

广义线性模型代考

广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

有限元方法代写

随机分析代写

时间序列分析代写

回归分析代写

MATLAB代写

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

cs代写|复杂网络代写complex network代考|CS7280

Posted on 2022年9月1日2022年9月1日 by statistics-lab

如果你也在怎样代写复杂网络complex network这个学科遇到相关的难题，请随时右上角联系我们的24/7代写客服。

我们提供的复杂网络complex network及其相关学科的代写，服务范围广, 其中包括但不限于:

Statistical Inference 统计推断
Statistical Computing 统计计算
Advanced Probability Theory 高等概率论
Advanced Mathematical Statistics 高等数理统计学
(Generalized) Linear Models 广义线性模型
Statistical Machine Learning 统计机器学习
Longitudinal Data Analysis 纵向数据分析
Foundations of Data Science 数据科学基础

cs代写|复杂网络代写complex network代考|SYNCHRONIZATION OF COMPLEX NETWORKS

In the field of complex networks’ synchronization with switching topologies, a wide range of research has been recently focused on dealing with issues related to the switchings and their effects on synchronization.

There has been increasing recognition that each topology candidate’s properties and the switching strategy for topologies play essential roles in achieving synchronization for complex networks with switching topologies. The analytical approaches for synchronization of continuous- and discrete-time complex networks with switching topologies are generally different. Mathematically, the continuous-time complex network with switching topologies is a special kind of those with time-varying topology. However, it is preliminarily assumed in some existing works on synchronization of continuous-time network systems with time-varying topology that the connection links evolve continuously over time with a known bound for the changing rate [103] or with a time-varying Laplacian matrix being simultaneously diagonalizable [11]. Thus, the techniques developed in these works to solve synchronization problem of complex networks with special time-varying topology are generally hard to apply to that with switching topologies, especially to the case with directed switching topologies.

Specifically, averaging-based approaches were developed to analyze synchronization of continuous-time complex networks with fast switching topologies $[7,140]$ while multiple Lyapunov functions (MLFs)-based approaches were developed to analyze synchronization of continuous-time complex networks with slowly switching topologies (especially for the case with directed switching topologies) [190]. Furthermore, MLFLs-based approaches were usually employed to analyze synchronization of continuous-time complex networks with switching topologies under delayed or sampled-data coupling [90, 187]. Common Lyapunov function (CLF)- and functional (CLFL)-based approaches are applicable only to some special continuous-time complex networks with switching topologies such as each possible topology candidate is undirected [222].

cs代写|复杂网络代写complex network代考|CONSENSUS OF MASS WITH SWITCHING TOPOLOGIES

Since the pioneer works [65] in which heading consensus of the linearized Vicsek’s model was analyzed, consensus of MASs with switching topologies has attracted increasing attention from a wide range of scientific interests.

Consensus of first-order MASs with switching topologies: In the year of 2004, consensus problem of continuous time first-order (integrator-type) MASs with directed switching and balanced topology was formulated and studied in [116]. Due to the balanced property of each possible topology candidate, a common Lyapunov function was constructed in [116] for analyzing the convergence behaviors of disagreement vector. Consensus of both continuous- and discrete time first-order MASs with directed switching topologies was further studied in [128] where each possible topology candidate is not required to be balanced. By using graphical approaches, some interesting issues on consensus of a class of first-order MASs with switching topologies were further addressed in [13]. By employing a CLFL based approach, it was proven in [83] that average consensus in continuous time first-order MASs with time delayed protocol can be achieved if each topology candidate is strongly connected and balanced, and some linear matrix inequalities hold. Note that most of the aforementioned results are concerned with consensus of first-order MASs with deterministically switching topologies. However, considering the underlying topology may randomly switch among a set of topology candidates in some practical applications, there have been a number of results focusing on consensus of first-order MASs with randomly switching topologies $[54,155,156]$.

Consensus of second-order MASs with switching topologies: Based on the stability results for switched systems provided in [108], some dwell time (DT) based criteria for consensus of continuous time second-order MASs under directed switching topologies were established in [129] where it was revealed that consensus can be achieved if each topology candidate contains a directed spanning tree and the DT for switchings among different topology candidates is larger than a threshold value. When the graph describing the communication topology among followers is undirected, it was proven in [59] by constructing a CLF that leader-following consensus could be achieved if the topology jointly contains a directed spanning tree. Later, leader-following consensus problem of MASs with switching jointly reachable interconnection and transmission delays was solved in [234] by designing the switching laws among topology candidates, where the dynamics of the leader are described by first-order integrator.

复杂网络代写

cs代写|复杂网络代写complex network代考|SYNCHRONIZATION OF COMPLEX NETWORKS

在复杂网络与交换拓扑的同步领域，最近广泛的研究集中在处理与交换相关的问题及其对同步的影响。

人们越来越认识到，每个拓扑候选的属性和拓扑的交换策略在实现具有交换拓扑的复杂网络的同步方面起着至关重要的作用。具有交换拓扑的连续和离散时间复杂网络同步的分析方法通常是不同的。在数学上，具有交换拓扑的连续时间复杂网络是具有时变拓扑的一种特殊网络。然而，在一些现有的关于具有时变拓扑的连续时间网络系统同步的工作中，初步假设连接链路随着时间的推移以已知的变化率[103]或时变拉普拉斯矩阵不断演变同时可对角化[11]。因此，

具体来说，开发了基于平均的方法来分析具有快速切换拓扑的连续时间复杂网络的同步[7,140]同时开发了多个基于 Lyapunov 函数 (MLF) 的方法来分析具有缓慢切换拓扑的连续时间复杂网络的同步（特别是对于有向切换拓扑的情况）[190]。此外，基于 MLFL 的方法通常用于分析在延迟或采样数据耦合下具有切换拓扑的连续时间复杂网络的同步 [90, 187]。基于通用 Lyapunov 函数 (CLF) 和泛函 (CLFL) 的方法仅适用于一些具有切换拓扑的特殊连续时间复杂网络，例如每个可能的拓扑候选者都是无向的 [222]。

cs代写|复杂网络代写complex network代考|CONSENSUS OF MASS WITH SWITCHING TOPOLOGIES

自从分析了线性化 Vicsek 模型的标题一致性的先驱工作 [65] 以来，具有切换拓扑的 MAS 的一致性越来越受到广泛科学兴趣的关注。

具有切换拓扑的一阶 MAS 的共识：2004 年，在 [116] 中制定和研究了具有定向切换和平衡拓扑的连续时间一阶（积分器型）MAS 的共识问题。由于每个可能的拓扑候选者的平衡特性，在[116]中构建了一个通用的 Lyapunov 函数来分析分歧向量的收敛行为。在 [128] 中进一步研究了具有定向开关拓扑的连续和离散时间一阶 MAS 的共识，其中不需要平衡每个可能的拓扑候选。通过使用图形方法，在 [13] 中进一步解决了关于具有切换拓扑的一类一阶 MAS 共识的一些有趣问题。通过采用基于 CLFL 的方法，在 [83] 中证明，如果每个拓扑候选者都是强连接和平衡的，并且一些线性矩阵不等式成立，则可以在具有时间延迟协议的连续时间一阶 MAS 中实现平均共识。请注意，上述大多数结果都与具有确定性切换拓扑的一阶 MAS 的共识有关。然而，考虑到在一些实际应用中，底层拓扑可能在一组候选拓扑之间随机切换，已经有许多结果集中在随机切换拓扑的一阶 MAS 的一致性上。请注意，上述大多数结果都与具有确定性切换拓扑的一阶 MAS 的共识有关。然而，考虑到在一些实际应用中，底层拓扑可能在一组候选拓扑之间随机切换，已经有许多结果集中在随机切换拓扑的一阶 MAS 的一致性上。请注意，上述大多数结果都与具有确定性切换拓扑的一阶 MAS 的共识有关。然而，考虑到在一些实际应用中，底层拓扑可能在一组候选拓扑之间随机切换，已经有许多结果集中在随机切换拓扑的一阶 MAS 的一致性上。[54,155,156].

具有切换拓扑的二阶 MAS 的共识：基于 [108] 中提供的切换系统的稳定性结果，在 [129 ] 其中表明，如果每个拓扑候选者包含有向生成树，并且用于在不同拓扑候选者之间切换的 DT 大于阈值，则可以达成共识。当描述跟随者之间的通信拓扑的图是无向的时，在[59]中通过构造一个 CLF 证明，如果拓扑共同包含有向生成树，则可以实现领导者跟随共识。

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。

金融工程代写

非参数统计代写

非参数统计指的是一种统计方法，其中不假设数据来自于由少数参数决定的规定模型；这种模型的例子包括正态分布模型和线性回归模型。

广义线性模型代考

广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

有限元方法代写

随机分析代写

时间序列分析代写

回归分析代写

MATLAB代写

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

cs代写|复杂网络代写complex network代考|CS60078

Posted on 2022年9月1日2022年9月1日 by statistics-lab

如果你也在怎样代写复杂网络complex network这个学科遇到相关的难题，请随时右上角联系我们的24/7代写客服。

我们提供的复杂网络complex network及其相关学科的代写，服务范围广, 其中包括但不限于:

Statistical Inference 统计推断
Statistical Computing 统计计算
Advanced Probability Theory 高等概率论
Advanced Mathematical Statistics 高等数理统计学
(Generalized) Linear Models 广义线性模型
Statistical Machine Learning 统计机器学习
Longitudinal Data Analysis 纵向数据分析
Foundations of Data Science 数据科学基础

cs代写|复杂网络代写complex network代考|COMPLEX NETWORK SYSTEMS

Far from being separate entities, many natural, social, and engineering systems can be considered as CNSs associated with tight interactions among neighboring entities within them $[3,10,18,29,39,50,92,120,130,141,175,194,201,211,219]$. Roughly speaking, a CNS refers to a networking system that consists of lots of interconnected agents, where each agent is an elementary element or a fundamental unit with detailed contents depending on the nature of the specific network under consideration [175]. For example, the Internet is a CNS of routers and computers connected by various physical or wireless links. The cell can be described by a CNS of chemicals connected by chemical interactions. The scientific citation network is a CNS of papers and books linked by citations among them. The WeChat social network is a CNS whose agents are users and whose edges represent the relationships among users, to name just a few.

With the aid of coordination with neighboring individuals, a CNS can exhibit fascinating cooperative behaviors far beyond the individuals’ inherent properties. Prototypical cooperative behaviors include synchronization $[38,95,101,177]$, consensus $[76,118,128]$, swarming $[48,115]$, flocking $[117,161]$. In this book, we focus on the CNSs which include complex networks and MASs as special cases. A lot of new research challenges have been raised about understanding the emergence mechanisms responsible for various collective behaviors as well as global statistical properties of CNSs $[3,15,114,178]$. Network science, as a strong interdisciplinary research field, has been established at the first several years of the 21st century [110]. It is increasingly recognized that a detailed study on cooperative dynamics of CNSs would not only help researchers understand the evolution mechanism for macroscopical cooperative behaviors, but also prompt the application of network science to solve various engineering problems, e.g., design of distributed sensor networks [135], formation control of multiple unmanned aerial vehicles [37], distributed localization [89], and load assignment of multiple energy storage units in modern power grid [191].

Among the various cooperative behaviors of CNSs, synchronization of complex networks and consensus of MASs are the most fundamental yet most important ones. Synchronization of complex networks exhibits the cooperative behavior that the states of all entities within these networks achieve an agreement on some quantities of interest. Compared with stability analysis of an isolated control plant, synchronization behavior analysis in CNSs are much more challenging as the synchronization process is determined by the evolution of network topology as well as the inherent dynamics of individual units within these network systems $[96,102,121,198,199]$. As a topic closely related to synchronization of complex networks, the consensus of MASs has recently gained much attention from various research fields, especially the system science, control theory, and electrical engineering communities $[22,65,88,116,128]$. In the remainder of this chapter, we will review some existing results on achieving synchronization of complex networks and consensus of MASs over dynamically changing communication topologies.

cs代写|复杂网络代写complex network代考|DEFINITIONS OF SYNCHRONIZATION AND CONSENSUS

Before moving forward, the definition of consensus of MASs is given. Moreover, the synchronization of complex networks can be defined similarly.

Consider an MAS which consists of $N$ agents. Without loss of generality, we label the $N$ agents as agents $1, \ldots, N$, respectively. The dynamics of agent $i, i=1, \ldots, N$, are represented by
$$
\dot{x}_i(t)=f\left(t, x_i(t), u_i(t)\right),
$$
where $x_i(t) \in \mathbb{R}^n$ and $u_i(t) \in \mathbb{R}^m$ represent, respectively, the state and the control input, $f(\cdot, \cdot \cdot):\left[t_0,+\infty\right) \times \mathbb{R}^n \times \mathbb{R}^m \mapsto \mathbb{R}^n$ represents the nonlinear dynamics of agent i. A particular case is the general linear time-invariant MASs with the dynamics of agent $i$ are described by
$$
\dot{x}_i(t)=A x_i(t)+B u_i(t), i=1, \ldots, N,
$$
where $A \in \mathbb{R}^{n \times n}$ and $B \in \mathbb{R}^{n \times m}$ represent, respectively, the state matrix and control input matrix. For convenience, throughout this book, we call MAS (1.1) to represent the MAS whose dynamics are described by (1.1).

Definition $1.1$ Consensus of the MAS (1.1) is said to be achieved if for arbitrary initial conditions $x_i\left(t_0\right), i=1, \ldots, N$,
$$
\lim _{t \rightarrow \infty}\left|x_i(t)-x_j(t)\right|=0, i, j=1, \ldots, N .
$$
The definition of consensus for MAS (1.1) given by Eq. (1.3) does not concern about the final consensus states. However, it is sometimes important to make the states of all agents in the considered MASs to finally converge to some predesigned trajectory, especially from the viewpoint of controlling various complex engineering systems. To ensure the states of all agents in MAS (1.1) converge to some desired states, a target system (may be virtual) is introduced to the network (1.1) as
$$
\dot{s}(t)=f(t, s(t))
$$
for some given initial value $s\left(t_0\right) \in \mathbb{R}^n$. Under this scenario, we call agent $i$ whose dynamics are described by (1.1) the follower $i, i=1, \ldots, N$, and call the agent whose dynamics are described by (1.4) the leader.

复杂网络代写

cs代写|复杂网络代写complex network代考|COMPLEX NETWORK SYSTEMS

许多自然、社会和工程系统远不是独立的实体，而是可以被视为与其中相邻实体之间紧密相互作用相关的 CNS[3,10,18,29,39,50,92,120,130,141,175,194,201,211,219]. 粗略地说，CNS 是指由许多相互连接的代理组成的网络系统，其中每个代理是一个基本元素或基本单元，其详细内容取决于所考虑的特定网络的性质[175]。例如，互联网是通过各种物理或无线链路连接的路由器和计算机的 CNS。细胞可以通过化学相互作用连接的化学中枢神经系统来描述。科学引文网络是论文和书籍之间通过引文链接的 CNS。微信社交网络是一个CNS，其代理是用户，其边代表用户之间的关系，仅举几例。

借助与邻近个体的协调，CNS 可以表现出远远超出个体固有属性的迷人合作行为。典型的合作行为包括同步[38,95,101,177]，共识[76,118,128], 蜂拥而至[48,115], 植绒[117,161]. 在本书中，我们重点关注包含复杂网络和 MAS 的 CNS 作为特例。关于理解负责各种集体行为的出现机制以及 CNS 的全局统计特性，已经提出了许多新的研究挑战[3,15,114,178]. 网络科学作为一个强大的跨学科研究领域，在 21 世纪头几年已经确立[110]。人们越来越认识到，对 CNS 合作动力学的详细研究不仅有助于研究人员了解宏观合作行为的演化机制，而且还促进网络科学应用于解决各种工程问题，例如分布式传感器网络的设计 [135 ]，多架无人机编队控制[37]，分布式定位[89]，现代电网中多个储能单元的负荷分配[191]。

在 CNS 的各种协作行为中，复杂网络的同步和 MAS 的共识是最基本也是最重要的。复杂网络的同步展示了这些网络中所有实体的状态就某些感兴趣的数量达成一致的合作行为。与孤立控制设备的稳定性分析相比，CNS 中的同步行为分析更具挑战性，因为同步过程取决于网络拓扑的演变以及这些网络系统中各个单元的固有动态[96,102,121,198,199]. 作为与复杂网络同步密切相关的课题，MASs 的共识最近引起了各个研究领域的广泛关注，尤其是系统科学、控制理论和电气工程界。[22,65,88,116,128]. 在本章的剩余部分，我们将回顾一些关于实现复杂网络同步和 MAS 在动态变化的通信拓扑上的共识的现有结果。

cs代写|复杂网络代写complex network代考|DEFINITIONS OF SYNCHRONIZATION AND CONSENSUS

在继续之前，给出了MAS共识的定义。此外，可以类似地定义复杂网络的同步。
考虑一个 MAS，它由 $N$ 代理。不失一般性，我们标记 $N$ 代理人作为代理人 $1, \ldots, N$ ，分别。代理的动态 $i, i=1, \ldots, N$, 表示为
$$
\dot{x}i(t)=f\left(t, x_i(t), u_i(t)\right), $$ 在哪里 $x_i(t) \in \mathbb{R}^n$ 和 $u_i(t) \in \mathbb{R}^m$ 分别表示状态和控制输入， $f(\cdot, \cdot):\left[t_0,+\infty\right) \times \mathbb{R}^n \times \mathbb{R}^m \mapsto \mathbb{R}^n$ 表示代理 $i$ 的非线性动力学。一个特例是具有代理动力学的一般线性时不变 MAS $i$ 被描述为 $$ \dot{x}_i(t)=A x_i(t)+B u_i(t), i=1, \ldots, N, $$ 在哪里 $A \in \mathbb{R}^{n \times n}$ 和 $B \in \mathbb{R}^{n \times m}$ 分别表示状态矩阵和控制输入矩阵。为方便起见，在整本书中，我们称 MAS (1.1) 来表示其动力学由 (1.1) 描述的 MAS。定义 $1.1$ 如果对于任意初始条件，则据说可以实现 MAS (1.1) 的共识 $x_i\left(t_0\right), i=1, \ldots, N$ ， $$ \lim {t \rightarrow \infty}\left|x_i(t)-x_j(t)\right|=0, i, j=1, \ldots, N .
$$
由方程式给出的 MAS (1.1) 共识的定义。(1.3) 不关心最终共识状态。然而，有时重要的是使所考虑的 MAS 中的所有代理的状态最终收敛到某个预先设计的轨迹，特别是从控制各种复杂工程系统的角度来看。为了确保 MAS (1.1) 中所有代理的状态收敛到一些期望的状态，将目标系统（可能是虚拟的) 引入网络 (1.1) 为
$$
\dot{s}(t)=f(t, s(t))
$$
对于一些给定的初始值 $s\left(t_0\right) \in \mathbb{R}^n$. 在这种情况下，我们称代理 $i$ 其动力学由 $(1.1)$ 跟随者描述 $i, i=1, \ldots, N$ ，并调用由 (1.4) 描述动态的智能体为领导者。

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。

金融工程代写

非参数统计代写

非参数统计指的是一种统计方法，其中不假设数据来自于由少数参数决定的规定模型；这种模型的例子包括正态分布模型和线性回归模型。

广义线性模型代考

广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

有限元方法代写

随机分析代写

时间序列分析代写

回归分析代写

MATLAB代写

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

CS代写|程序设计作业代写algorithm Programming代考|CS561

Posted on 2022年7月8日2022年7月8日 by statistics-lab

如果你也在怎样代写程序设计algorithm Programming这个学科遇到相关的难题，请随时右上角联系我们的24/7代写客服。

程序设计是用于解决一个问题的程序或公式。它是基于进行一连串指定的行动，这些行动描述了如何做某事，而你的计算机每次都会以这种方式做。

statistics-lab™ 为您的留学生涯保驾护航在代写程序设计algorithm Programming方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写程序设计algorithm Programming代写方面经验极为丰富，各种代写程序设计algorithm Programming相关的作业也就用不着说。

我们提供的程序设计algorithm Programming及其相关学科的代写，服务范围广, 其中包括但不限于:

Statistical Inference 统计推断
Statistical Computing 统计计算
Advanced Probability Theory 高等概率论
Advanced Mathematical Statistics 高等数理统计学
(Generalized) Linear Models 广义线性模型
Statistical Machine Learning 统计机器学习
Longitudinal Data Analysis 纵向数据分析
Foundations of Data Science 数据科学基础

CS代写|程序设计作业代写algorithm Programming代考|CS561

CS代写|程序设计作业代写algorithm Programming代考|Not That Kind of Graph

Your task is to graph the price of a stock over time. In one unit of time, the stock can either Rise, Fall, or stay Constant. The stock’s price will be given to you as a string of R’s, F’s, and C’s. You need to graph it using the characters ‘ $/$ ‘ (slash), ‘ $R$ ‘ (backslash) and ‘_’ (underscore).
Input
The first line of input gives the number of cases, $N$. $N$ test cases follow. Each one contains a string of at least 1 and at most 50 uppercase characters ( $R, F$, or $C$ ).
Output
For each test case, output the line “Case #x:”, where $x$ is the number of the test case. Then print the graph, as shown in the sample output, including the $x$ – and $y$-axes. The $x$-axis should be one character longer than the graph, and there should be one space between the $y$-axis and the start of the graph. There should be no trailing spaces on any line. Do not print unnecessary lines. The $x$-axis should always appear directly below the graph. Finally, print an empty line after each test case.

CS代写|程序设计作业代写algorithm Programming代考|Factorial! You Must be Kidding

Arif has bought a supercomputer from Bongobazar. Bongobazar is a place in Dhaka where secondhand goods are available. So the supercomputer he bought is also secondhand and has some bugs. One of the bugs is that the range of unsigned long integers of this computer for a $\mathrm{C} / \mathrm{C}++$ compiler has changed. Now its new lower limit is 10000 and the upper limit is 6227020800 . Arif writes a program in C/C++ which determines the factorial of an integer. The factorial of an integer is defined recursively as:
Factorial $(0)=1$
Factorial $(n)=n \times$ Factorial $(n-1)$.
Of course, one can manipulate these expressions. For example, it can be written as:
$$
\text { Factorial }(n)=n \times(n-1) \times \text { Factorial }(n-2)
$$
This definition can also be converted to an iterative one.
But Arif knows that his program will not behave correctly in the supercomputer. You are to write a program which will simulate that changed behavior in a normal computer.
Input
The input file contains several lines of input. Each line contains a single integer $n$. No integer has more than 6 digits. Input is terminated by end of file.

程序设计代写

CS代写|程序设计作业代写algorithm Programming代考|Not That Kind of Graph

您的任务是绘制股票价格随时间变化的图表。在一个单位时间内，股票可以上涨、下跌或保持不变。股票的价格将以 R、F和C 的字符串形式提供给您。您需要使用字符 ‘ 绘制它/’ (斜线)， ‘ $R$ ‘ (反斜杜) 和’.’ (下划线)。输入
第一行输入给出了案例的数量， $N . N$ 测试用例如下。每个都包含至少 1 个和最多 50 个大写字符的字符串 $(R, F$ 或者 $C)$ 。
输出
对于每个测试用例，输出”Case #x:”行，其中 $x$ 是测试用例的编号。然后打印图形，如示例输出所示，包括 $x$ – 和 $y$ 轴。这 $x$-axis应该比图表长一个字符，并且应该有一个空格。 $y$-轴和图表的开始。任何一行都不应有尾随空格。不要打印不必要的行。这 $x$-axis 应始终显示在图形的正下方。最后，在每个测试用例之后打印一个空行。

CS代写|程序设计作业代写algorithm Programming代考|Factorial! You Must be Kidding

Arif 从 Bongobazar 购买了一台超级计算机。Bongobazar 是达卡的一个出售二手商品的地方。所以他买的超级电脑也是二手的，有一些bug。错误之一是这台计算机的无符号长整数范围 $\mathrm{C} / \mathrm{C}++$ 编译器变了。现在它的新下限是 10000 ，上限是 6227020800 。Arif 用 C/C++ 编写了一个确定整数阶乘的程序。
整数的阶乘递归定义为: $(0)=1$
阶乘 $(n)=n \times$ 阶乘 $(n-1)$.
当然，可以操纵这些表达式。例如，它可以写成:
Factorial $(n)=n \times(n-1) \times$ Factorial $(n-2)$
此定义也可以转换为迭代定义。
但是 Arif 知道他的程序在超级计算机中无法正常运行。您将编写一个程序来模拟普通计算机中的更改行为。输入
输入文件包含几行输入。每行包含一个整数 $n$. 没有整数超过 6 位。输入在文件末尾终止。

CS代写|程序设计作业代写algorithm Programming代考请认准statistics-lab™

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。

金融工程代写

非参数统计代写

非参数统计指的是一种统计方法，其中不假设数据来自于由少数参数决定的规定模型；这种模型的例子包括正态分布模型和线性回归模型。

广义线性模型代考

广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

有限元方法代写

随机分析代写

时间序列分析代写

回归分析代写

MATLAB代写

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

CS代写|程序设计作业代写algorithm Programming代考|COMP110

Posted on 2022年7月8日2022年7月8日 by statistics-lab

如果你也在怎样代写程序设计algorithm Programming这个学科遇到相关的难题，请随时右上角联系我们的24/7代写客服。

程序设计是用于解决一个问题的程序或公式。它是基于进行一连串指定的行动，这些行动描述了如何做某事，而你的计算机每次都会以这种方式做。

我们提供的程序设计algorithm Programming及其相关学科的代写，服务范围广, 其中包括但不限于:

Statistical Inference 统计推断
Statistical Computing 统计计算
Advanced Probability Theory 高等概率论
Advanced Mathematical Statistics 高等数理统计学
(Generalized) Linear Models 广义线性模型
Statistical Machine Learning 统计机器学习
Longitudinal Data Analysis 纵向数据分析
Foundations of Data Science 数据科学基础

CS代写|程序设计作业代写algorithm Programming代考|COMP110

CS代写|程序设计作业代写algorithm Programming代考|Tic Tac Toe

Tic Tac Toe is a child’s game played on a 3 by 3 grid. One player, X, starts by placing an $\mathrm{X}$ at an unoccupied grid position. Then the other player, $\mathrm{O}$, places an $\mathrm{O}$ at an unoccupied grid position. Play alternates between $X$ and $O$ until the grid is filled or one player’s symbols occupy an entire line (vertical, horizontal, or diagonal) in the grid.

We will denote the initial empty Tic Tac Toe grid with nine dots. Whenever $\mathrm{X}$ or $\mathrm{O}$ plays, we fill in an $\mathrm{X}$ or an $\mathrm{O}$ in the appropriate position. The example in Figure $1.4$ illustrates each grid configuration from the beginning to the end of game in which $X$ wins.

Your job is to read a grid and to determine whether or not it could possibly be part of a valid Tic Tac Toe game. That is, is there a series of plays that can yield this grid somewhere between the start and end of the game?
Input
The first line of input contains $N$, the number of test cases. $4 N-1$ lines follow, specifying $N$ grid configurations separated by empty lines.

CS代写|程序设计作业代写algorithm Programming代考|Rock, Scissors, Paper

Bart’s sister Lisa has created a new civilization on a two-dimensional grid. At the outset, each grid location may be occupied by one of three life forms: Rocks, Scissors,or Papers. Each day, differing life forms occupying horizontally or vertically adjacent grid locations wage war. In each war, Rocks always defeat Scissors, Scissors always defeat Papers, and Papers always defeat Rocks. At the end of the day, the victor expands its territory to include the loser’s grid position. The loser vacates the position.

Your job is to determine the territory occupied by each life form after $n$ days.
Input
The first line of input contains $t$, the number of test cases. Each test case begins with three integers not greater than $100: r$ and $c$, the number of rows and columns in the grid, and $n$. The grid is represented by the $r$ lines that follow, each with $c$ characters. Each character in the grid is $R, S$, or $P$, indicating that it is occupied by Rocks, Scissors, or Papers respectively.

程序设计代写

CS代写|程序设计作业代写algorithm Programming代考|Tic Tac Toe

Tic Tac Toe 是一种儿童游戏，在 3 x 3 网格上进行。一名玩家 X 开始放置一个X在一个未被占用的网格位置。然后另一个玩家，○, 放置一个○在一个未被占用的网格位置。交替播放X和○直到网格被填满或一个玩家的符号占据网格中的整条线（垂直、水平或对角线）。

我们将用九个点表示初始空井字游戏网格。每当X或者○播放，我们填写一个X或一个○在适当的位置。图中的例子1.4说明了从游戏开始到结束的每个网格配置，其中X获胜。

您的工作是阅读网格并确定它是否可能是有效井字游戏的一部分。也就是说，是否有一系列的游戏可以在游戏开始和结束之间的某个地方产生这个网格？
输入输入
的第一行包含ñ，测试用例的数量。4ñ−1接下来的行，指定ñ由空线分隔的网格配置。

CS代写|程序设计作业代写algorithm Programming代考|Rock, Scissors, Paper

巴特的妹妹丽莎在二维网格上创造了一个新文明。一开始，每个网格位置可能被三种生命形式中的一种占据：石头、剪刀或纸。每天，占据水平或垂直相邻网格位置的不同生命形式发动战争。在每场战争中，Rocks 总是击败 Scissors，Scissors 总是击败 Papers，Papers 总是击败 Rocks。在一天结束时，胜利者将其领土扩大到包括失败者的网格位置。失败者腾出位置。

你的工作是确定每个生命形式在之后所占据的领土n天。
输入输入
的第一行包含吨，测试用例的数量。每个测试用例以三个不大于100:r和C，网格中的行数和列数，以及n. 网格由r接下来的行，每行都有C人物。网格中的每个字符都是R,小号，或者磷，表示它分别被 Rocks、Scissors 或 Papers 占据。

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写