# 计算机代写|密码学与网络安全代写cryptography and network security代考|CS388H

#### Doug I. Jones

Lorem ipsum dolor sit amet, cons the all tetur adiscing elit

couryes-lab™ 为您的留学生涯保驾护航 在代写密码学与网络安全cryptography and network security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。我们的专家在代写密码学与网络安全cryptography and network security代写方面经验极为丰富，各种代写密码学与网络安全cryptography and network security相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (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
couryes™为您提供可以保分的包课服务

## 计算机代写|密码学与网络安全代写cryptography and network security代考|The Concept of Information Theory

The concept of information transmission is associated with the existence of a communication channel that links the source and destination of the message. This can imply the occurrence of transmission errors, caused by the probabilistic nature of the channel.

Figure $5.1$ illustrates the canonical model for a communication channel, proposed by Shannon in his seminal paper of 1948 Shannon, 1948b. This is a very simplified model of reality but contains the basic blocks upon which the mathematical structure is built.

Consider two discrete and finite sample spaces, $\Omega$ and $\Psi$, with the associated random variables $X$ and $Y$,
\begin{aligned} &X=x_1, x_2, \ldots, x_N \ &Y=y_1, y_2, \ldots, y_M \end{aligned}
The events from $\Omega$ may jointly occur with events from $\Psi$. Therefore, the following matrix contains the whole set of events in the product space $\Omega \Psi$,

$$[X Y]=\left[\begin{array}{cccc} x_1 y_1 & x_1 y_2 & \cdots & x_1 y_M \ x_2 y_1 & x_2 y_2 & \cdots & x_2 y_M \ \cdots & \cdots & \cdots & \cdots \ x_N y_1 & x_N y_2 & \cdots & x_N y_M \end{array}\right]$$
The joint probability matrix is given in the following, in which no restriction is assumed regarding the dependence between the random variables
$$[\mathrm{P}(X, Y)]=\left[\begin{array}{cccc} p_{1,1} & p_{1,2} & \cdots & p_{1, M} \ p_{2,1} & p_{2,2} & \cdots & p_{2, M} \ \cdots & \cdots & \cdots & \cdots \ p_{N, 1} & p_{N, 2} & \cdots & p_{N, M} \end{array}\right]$$
Figure $5.2$ shows the relation between the input and output alphabets, which are connected by the joint probability distribution matrix $[\mathrm{P}(X, Y)]$.

## 计算机代写|密码学与网络安全代写cryptography and network security代考|Conditional Entropy

The concept of conditional entropy is essential to model, and understand, the operation of the communication channel because it provides information about a particular symbol, given that another symbol has occurred. The entropy of alphabet $X$ conditioned to the occurrence of a particular symbol $y$ is given by
\begin{aligned} H(X \mid y) &=-\sum_X \frac{p(x, y)}{p(y)} \log \frac{p(x, y)}{p(y)} \ &=-\sum_X p(x \mid y) \log p(x \mid y) \end{aligned}
The expected value of the conditional entropy, for all possibles values of $y$, provides the average conditional entropy of the system
\begin{aligned} H(X \mid Y)=E[H(X \mid y)] &=\sum_Y p(y)[H(X \mid y)] \ &=-\sum_Y p(y) \sum_X p(x \mid y) \log p(x \mid y) \end{aligned}
which can be written as
$$H(X \mid Y)=-\sum_Y \sum_X p(y) p(x \mid y) \log p(x \mid y)$$ or
$$H(X \mid Y)=-\sum_Y \sum_X p(x, y) \log p(x \mid y) .$$
In the same way, the mean conditional entropy of source $Y$, given the information about source $X$, is
$$H(Y \mid X)=-\sum_X \sum_Y p(x) p(y \mid x) \log p(y \mid x)$$
or
$$H(Y \mid X)=-\sum_X \sum_Y p(x, y) \log p(y \mid x)$$

# 密码学与网络安全代考

## 计算机代写|密码学与网络安全代写密码学与网络安全代考|信息论的概念

\begin{aligned} &X=x_1, x_2, \ldots, x_N \ &Y=y_1, y_2, \ldots, y_M \end{aligned}

$$[X Y]=\left[\begin{array}{cccc} x_1 y_1 & x_1 y_2 & \cdots & x_1 y_M \ x_2 y_1 & x_2 y_2 & \cdots & x_2 y_M \ \cdots & \cdots & \cdots & \cdots \ x_N y_1 & x_N y_2 & \cdots & x_N y_M \end{array}\right]$$

$$[\mathrm{P}(X, Y)]=\left[\begin{array}{cccc} p_{1,1} & p_{1,2} & \cdots & p_{1, M} \ p_{2,1} & p_{2,2} & \cdots & p_{2, M} \ \cdots & \cdots & \cdots & \cdots \ p_{N, 1} & p_{N, 2} & \cdots & p_{N, M} \end{array}\right]$$

## 计算机代写|密码学与网络安全代写密码与网络安全代考|条件熵

.计算机代写| 条件熵的概念对于建模和理解通信信道的运行是必不可少的，因为它提供了关于一个特定符号的信息，假设另一个符号已经发生。字母表的熵 $X$ 以特定符号的出现为条件的 $y$
\begin{aligned} H(X \mid y) &=-\sum_X \frac{p(x, y)}{p(y)} \log \frac{p(x, y)}{p(y)} \ &=-\sum_X p(x \mid y) \log p(x \mid y) \end{aligned}的所有可能值的条件熵的期望值 $y$，提供系统的平均条件熵
\begin{aligned} H(X \mid Y)=E[H(X \mid y)] &=\sum_Y p(y)[H(X \mid y)] \ &=-\sum_Y p(y) \sum_X p(x \mid y) \log p(x \mid y) \end{aligned}

$$H(X \mid Y)=-\sum_Y \sum_X p(y) p(x \mid y) \log p(x \mid y)$$ 或
$$H(X \mid Y)=-\sum_Y \sum_X p(x, y) \log p(x \mid y) .$$

$$H(Y \mid X)=-\sum_X \sum_Y p(x) p(y \mid x) \log p(y \mid x)$$

$$H(Y \mid X)=-\sum_X \sum_Y p(x, y) \log p(y \mid x)$$

## 有限元方法代写

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。

## 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。

Days
Hours
Minutes
Seconds

# 15% OFF

## On All Tickets

Don’t hesitate and buy tickets today – All tickets are at a special price until 15.08.2021. Hope to see you there :)