### 电子工程代写|数字信号处理代写Digital Signal Processing代考|EE615

statistics-lab™ 为您的留学生涯保驾护航 在代写数字信号处理Digital Signal 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。我们的专家在代写数字信号处理Digital Signal Processing方面经验极为丰富，各种代写数字信号处理Digital Signal Processing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

## 电子工程代写|数字信号处理代写Digital Signal Processing代考|Textbooks

The following textbooks are the most relevant for this module:

• “Essentials of Digital Signal Processing,” B.P. Lathi and R.A. Green, Cambridge University Press, 2014. Lathi has authored several popular textbooks on signals and systems. This recent text is quite accessible and features strong integration with MATLAB.
• “Essential MATLAB for engineers and scientists,” B. Hahn and D. Valentine, Academic Press, 7th Edition, 2019. There are many excellent free re-
• sources for MATLAB, including the official software documentation (go to the help browser within the software or visit https://uk . mathworks. com/help/ matlab/index.html). While this book has only a very brief chapter on signal processing, it is good for a broad overview of MATLAB if you are seeking a general reference. It is also up to date as of MATLAB release $2018 \mathrm{~b}$.
• “Discrete-Time Signal Processing,” Oppenheim and Schafer, Pearson, 3rd Edition, 2013. Every signal processing textbook will have its relative strengths and weaknesses. This book serves as an alternative to Lathi and Green’s “Essentials of Digital Signal Processing.” While MATLAB is used for some of the examples, it is not thoroughly integrated, but overall this book has greater depth and breadth of topics. For example, it provides better coverage of random processes and signals.
• We will refer to several other textbooks and resources throughout the module, but they will only be relevant for 1 or 2 lessons each. Please see “Further Reading” at the end of each lesson for details.

## 电子工程代写|数字信号处理代写Digital Signal Processing代考|Signals and Signal Classification

So what are signals? A signal is a quantity that can be varied in order to convey information. If a signal does not contain useful information (at least not in the current context), then the signal is regarded as noise. You may have a useful audio signal for your neighbour in a lecture, but this could be noise to anyone nearby that is trying to listen to the instructor!

Practically any physical phenomena can be understood as a signal (e.g., temperature, pressure, concentration, voltage, current, impedance, velocity, displacement, vibrations, colour). Immaterial quantities can also be signals (e.g., words, stock prices, module marks). Signals are usually described over time, frequency, and/or spatial domains. Time and frequency will be the most common in the context of this module, but our brief introduction to image processing will treat images as two-dimensional signals.

There are several ways of classifying signals. We will classify according to how they are defined over time and in amplitude. Over time we have:

1. Continuous-time signals – signals that are specified for every value of time $t$ (e.g., sound level in a classroom).
2. Discrete-time signals – signals that are specified at discrete values of time (e.g., the average daily temperature). The times are usually denoted by the integer $n$.
In amplitude we have:
3. Analogue signals – signals can have any value over a continuous range (e.g., body temperature).
4. Digital signals – signals whose amplitude is restricted to a finite number of values (e.g., the result of rolling a die).

While we can mix and match these classes of signals, in practice we most often see continuous-time analogue signals (i.e., many physical phenomena) and discrete-time digital signals (i.e., how signals are most easily represented in a computer); see Fig. 1.1. However, digital representations of data are often difficult to analyse mathematically, so we will usually treat them as if they were analogue. Thus, the key distinction is actually continuous-time versus discrete-time, even though for convenience we will refer to these as analogue and digital. The corresponding mathematics for continuous-time and discrete-time signals are distinct, and so they also impose the structure of this module.

# 数字信号处理代考

## 电子工程代写|数字信号处理代写Digital Signal Processing代考|Textbooks

• “Essentials of Digital Signal Processing”，BP Lathi 和 RA Green，剑桥大学出版社，2014 年。Lathi 撰写了多本关于信号和系统的热门教科书。这篇最近的文章很容易理解，并且与 MATLAB 紧密集成。
• “工程师和科学家的基本 MATLAB”，B. Hahn 和 D. Valentine，Academic Press，第 7 版，2019 年。
• MATLAB 的资源，包括官方软件文档（转到软件内的帮助浏览器或访问 https://uk.mathworks.com/help/matlab/index.html）。虽然这本书只有一个非常简短的章节介绍信号处理，但如果您正在寻找一般参考资料，那么它有助于对 MATLAB 进行广泛的概述。它也是最新的 MATLAB 版本2018 b.
• “离散时间信号处理”，Oppenheim 和 Schafer，Pearson，第 3 版，2013 年。每本信号处理教科书都有其相对优势和劣势。本书可替代 Lathi 和 Green 的“数字信号处理基础”。虽然 MATLAB 用于某些示例，但并未完全集成，但总体而言，本书的主题具有更大的深度和广度。例如，它可以更好地覆盖随机过程和信号。
• 我们将在整个模块中参考其他几本教科书和资源，但它们每本仅与 1 或 2 节课相关。详情请见每课后的“延伸阅读”。

## 电子工程代写|数字信号处理代写Digital Signal Processing代考|Signals and Signal Classification

1. 连续时间信号——为每个时间值指定的信号吨（例如，教室中的声级）。
2. 离散时间信号——以离散时间值指定的信号（例如，日平均温度）。次数通常用整数表示n.
在幅度方面，我们有：
3. 模拟信号——信号可以在连续范围内具有任何值（例如，体温）。
4. 数字信号——其幅度被限制在有限数量值内的信号（例如，掷骰子的结果）。

## 广义线性模型代考

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## 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。