## 计算机代写|计算机视觉代写Computer Vision代考|CS763

statistics-lab™ 为您的留学生涯保驾护航 在代写计算机视觉Computer Vision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。我们的专家在代写计算机视觉Computer Vision方面经验极为丰富，各种代写计算机视觉Computer Vision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

## 计算机代写|计算机视觉代写Computer Vision代考|Geometric primitives and transformations

In this section, we introduce the basic 2D and 3D primitives used in this textbook, namely points, lines, and planes. We also describe how 3D features are projected into 2D features. More detailed descriptions of these topics (along with a gentler and more intuitive introduction) can be found in textbooks on multiple-view geometry (Hartley and Zisserman 2004; Faugeras and Luong 2001).
Geometric primitives form the basic building blocks used to describe three-dimensional shapes. In this section, we introduce points, lines, and planes. Later sections of the book discuss curves (Sections $7.3$ and 12.2), surfaces (Section 13.3), and volumes (Section 13.5).
2D points. 2D points (pixel coordinates in an image) can be denoted using a pair of values, $\mathbf{x}=(x, y) \in \mathcal{R}^2$, or alternatively,
$$\mathbf{x}=\left[\begin{array}{l} x \ y \end{array}\right]$$
(As stated in the introduction, we use the $\left(x_1, x_2, \ldots\right)$ notation to denote column vectors.)

2D points can also be represented using homogeneous coordinates, $\tilde{\mathbf{x}}=(\tilde{x}, \tilde{y}, \tilde{w}) \in \mathcal{P}^2$, where vectors that differ only by scale are considered to be equivalent. $\mathcal{P}^2=\mathcal{R}^3-(0,0,0)$ is called the 2D projective space.

A homogeneous vector $\tilde{\mathbf{x}}$ can be converted back into an inhomogeneous vector $\mathbf{x}$ by dividing through by the last element $\tilde{w}$, i.e.,
$$\tilde{\mathbf{x}}=(\tilde{x}, \tilde{y}, \tilde{w})=\tilde{w}(x, y, 1)=\tilde{w} \overline{\mathbf{x}},$$
where $\overline{\mathbf{x}}=(x, y, 1)$ is the augmented vector. Homogeneous points whose last element is $\tilde{w}=0$ are called ideal points or points at infinity and do not have an equivalent inhomogeneous representation.
2D lines. 2D lines can also be represented using homogeneous coordinates $\tilde{\mathbf{I}}=(a, b, c)$. The corresponding line equation is
$$\overline{\mathbf{x}} \cdot \tilde{\mathbf{l}}=a x+b y+c=0 .$$
We can normalize the line equation vector so that $\mathbf{l}=\left(\hat{n}_x, \hat{n}_y, d\right)=(\hat{\mathbf{n}}, d)$ with $|\hat{\mathbf{n}}|=1$. In this case, $\hat{\mathbf{n}}$ is the normal vector perpendicular to the line and $d$ is its distance to the origin (Figure 2.2). (The one exception to this normalization is the line at infinity $\tilde{\mathbf{l}}=(0,0,1)$, which includes all (ideal) points at infinity.)

We can also express $\hat{\mathbf{n}}$ as a function of rotation angle $\theta, \hat{\mathbf{n}}=\left(\hat{n}_x, \hat{n}_y\right)=(\cos \theta, \sin \theta)$ (Figure 2.2a). This representation is commonly used in the Hough transform line-finding algorithm, which is discussed in Section 7.4.2. The combination $(\theta, d)$ is also known as polar coordinates.
When using homogeneous coordinates, we can compute the intersection of two lines as
$$\tilde{\mathbf{x}}=\tilde{\mathbf{l}}_1 \times \tilde{\mathbf{l}}_2,$$
where $\times$ is the cross product operator. Similarly, the line joining two points can be written as
$$\tilde{\mathbf{l}}=\tilde{\mathbf{x}}_1 \times \tilde{\mathbf{x}}_2 .$$
When trying to fit an intersection point to multiple lines or, conversely, a line to multiple points, least squares techniques (Section 8.1.1 and Appendix A.2) can be used, as discussed in Exercise 2.1.

## 计算机代写|计算机视觉代写Computer Vision代考|2D transformations

Having defined our basic primitives, we can now turn our attention to how they can be transformed. The simplest transformations occur in the 2D plane are illustrated in Figure 2.4.
Translation. $2 \mathrm{D}$ translations can be written as $\mathbf{x}^{\prime}=\mathrm{x}+\mathbf{t}$ or
$$\mathbf{x}^{\prime}=\left[\begin{array}{ll} \mathbf{l} & \mathbf{t} \end{array}\right] \overline{\mathbf{x}},$$
where $I$ is the $(2 \times 2)$ identity matrix or
$$\overline{\mathbf{x}}^{\prime}=\left[\begin{array}{cc} \mathbf{I} & \mathbf{t} \ \mathbf{0}^T & 1 \end{array}\right] \overline{\mathbf{x}},$$
where $\mathbf{0}$ is the zero vector. Using a $2 \times 3$ matrix results in a more compact notation, whereas using a full-rank $3 \times 3$ matrix (which can be obtained from the $2 \times 3$ matrix by appending a [0 $\left.0^T 1\right]$ row) makes it possible to chain transformations using matrix multiplication as well as to compute inverse transforms. Note that in any equation where an augmented vector such as $\overline{\mathbf{x}}$ appears on both sides, it can always be replaced with a full homogeneous vector $\tilde{\mathbf{x}}$.

# 计算机视觉代考

## 计算机代写|计算机视觉代写Computer Vision代考|Geometric primitives and transformations

$$\mathbf{x}=\left[\begin{array}{ll} x & y \end{array}\right]$$
（如介绍中所述，我们使用 $\left(x_1, x_2, \ldots\right)$ 表示列向量的符号。)

$$\tilde{\mathbf{x}}=(\tilde{x}, \tilde{y}, \tilde{w})=\tilde{w}(x, y, 1)=\tilde{w} \overline{\mathbf{x}},$$

$$\overline{\mathbf{x}} \cdot \tilde{\mathbf{l}}=a x+b y+c=0 .$$

$$\tilde{\mathbf{x}}=\tilde{\mathbf{l}}_1 \times \tilde{\mathbf{l}}_2$$

$$\tilde{\mathbf{1}}=\tilde{\mathbf{x}}_1 \times \tilde{\mathbf{x}}_2 .$$

## 计算机代写|计算机视觉代写Computer Vision代考|2D transformations

$$\mathbf{x}^{\prime}=\left[\begin{array}{ll} \mathbf{1} & \mathbf{t} \end{array}\right] \overline{\mathbf{x}}$$

$$\overline{\mathbf{x}}^{\prime}=\left[\begin{array}{lll} \mathbf{I} & \mathbf{t} \mathbf{0}^T & 1 \end{array}\right] \overline{\mathbf{x}}$$

## 广义线性模型代考

statistics-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。

## 计算机代写|计算机视觉代写Computer Vision代考|CPS843

statistics-lab™ 为您的留学生涯保驾护航 在代写计算机视觉Computer Vision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。我们的专家在代写计算机视觉Computer Vision方面经验极为丰富，各种代写计算机视觉Computer Vision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

## 计算机代写|计算机视觉代写Computer Vision代考|Book overview

In the final part of this introduction, I give a brief tour of the material in this book, as well as a few notes on notation and some additional general references. Since computer vision is such a broad field, it is possible to study certain aspects of it, e.g., geometric image formation and 3D structure recovery, without requiring other parts, e.g., the modeling of reflectance and shading. Some of the chapters in this book are only loosely coupled with others, and it is not strictly necessary to read all of the material in sequence.

Figure $1.12$ shows a rough layout of the contents of this book. Since computer vision involves going from images to both a semantic understanding as well as a 3D structural description of the scene, I have positioned the chapters horizontally in terms of where in this spectrum they land, in addition to vertically according to their dependence. ${ }^9$

Interspersed throughout the book are sample applications, which relate the algorithms and mathematical material being presented in various chapters to useful, real-world applications. Many of these applications are also presented in the exercises sections, so that students can write their own.
At the end of each section, I provide a set of exercises that the students can use to implement, test, and refine the algorithms and techniques presented in each section. Some of the exercises are suitable as written homework assignments, others as shorter one-week projects, and still others as

open-ended research problems that make for challenging final projects. Motivated students who implement a reasonable subset of these exercises will, by the end of the book, have a computer vision software library that can be used for a variety of interesting tasks and projects.

If the students or curriculum do not have a strong preference for programming languages, Python, with the NumPy scientific and array arithmetic library plus the OpenCV vision library, are a good environment to develop algorithms and learn about vision. Not only will the students learn how to program using array/tensor notation and linear/matrix algebra (which is a good foundation for later use of PyTorch for deep learning), you can also prepare classroom assignments using Jupyter notebooks, giving you the option to combine descriptive tutorials, sample code, and code to be extended/modified in one convenient location. ${ }^{10}$

As this is a reference book, I try wherever possible to discuss which techniques and algorithms work well in practice, as well as provide up-to-date pointers to the latest research results in the areas that I cover. The exercises can be used to build up your own personal library of self-tested and validated vision algorithms, which is more worthwhile in the long term (assuming you have the time) than simply pulling algorithms out of a library whose performance you do not really understand.
The book begins in Chapter 2 with a review of the image formation processes that create the images that we see and capture. Understanding this process is fundamental if you want to take a scientific (model-based) approach to computer vision. Students who are eager to just start implementing algorithms (or courses that have limited time) can skip ahead to the next chapter and dip into this material later. In Chapter 2, we break down image formation into three major components. Geometric image formation (Section 2.1) deals with points, lines, and planes, and how these are mapped onto images using projective geometry and other models (including radial lens distortion). Photometric image formation (Section 2.2) covers radiometry, which describes how light interacts with surfaces in the world, and optics, which projects light onto the sensor plane. Finally, Section $2.3$ covers how sensors work, including topics such as sampling and aliasing, color sensing, and in-camera compression.

## 计算机代写|计算机视觉代写Computer Vision代考|Sample syllabus

Teaching all of the material covered in this book in a single quarter or semester course is a Herculean task and likely one not worth attempting. ${ }^{11}$ It is better to simply pick and choose topics related to the lecturer’s preferred emphasis and tailored to the set of mini-projects envisioned for the students.
Steve Seitz and I have successfully used a 10-week syllabus similar to the one shown in Table $1.1$ as both an undergraduate and a graduate-level course in computer vision. The undergraduate course $^{12}$ tends to go lighter on the mathematics and takes more time reviewing basics, while the graduate-level course ${ }^{13}$ dives more deeply into techniques and assumes the students already have a decent grounding in either vision or related mathematical techniques. Related courses have also been taught on the topics of 3D photography and computational photography. Appendix C.3 and the book’s website list other courses that use this book to teach a similar curriculum.

When Steve and I teach the course, we prefer to give the students several small programming assignments early in the course rather than focusing on written homework or quizzes. With a suitable choice of topics, it is possible for these projects to build on each other. For example, introducing feature matching early on can be used in a second assignment to do image alignment and stitching. Alternatively, direct (optical flow) techniques can be used to do the alignment and more focus can be put on either graph cut seam selection or multi-resolution blending techniques.

In the past, we have also asked the students to propose a final project (we provide a set of suggested topics for those who need ideas) by the middle of the course and reserved the last week of the class for student presentations. Sometimes, a few of these projects have actually turned into conference submissions!

No matter how you decide to structure the course or how you choose to use this book, I encourage you to try at least a few small programming tasks to get a feel for how vision techniques work and how they fail. Better yet, pick topics that are fun and can be used on your own photographs, and try to push your creative boundaries to come up with surprising results.

# 计算机视觉代考

## 计算机代写|计算机视觉代写Computer Vision代考|Sample syllabus

Steve Seitz 和我成功地使用了类似于表中所示的为期 10 周的教学大纲1.1作为计算机视觉的本科和研究生课程。本科课程12倾向于在数学上变得更轻松，并且需要更多时间复习基础知识，而研究生水平的课程13更深入地研究技术，并假设学生已经在视觉或相关数学技术方面有良好的基础。还教授了有关 3D 摄影和计算摄影主题的相关课程。附录 C.3 和本书的网站列出了使用本书教授类似课程的其他课程。

## 广义线性模型代考

statistics-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。

## 计算机代写|计算机视觉代写Computer Vision代考|CMSC426

statistics-lab™ 为您的留学生涯保驾护航 在代写计算机视觉Computer Vision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。我们的专家在代写计算机视觉Computer Vision方面经验极为丰富，各种代写计算机视觉Computer Vision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

## 计算机代写|计算机视觉代写Computer Vision代考|What is computer vision

As humans, we perceive the three-dimensional structure of the world around us with apparent ease. Think of how vivid the three-dimensional percept is when you look at a vase of flowers sitting on the table next to you. You can tell the shape and translucency of each petal through the subtle patterns of light and shading that play across its surface and effortlessly segment each flower from the background of the scene (Figure 1.1). Looking at a framed group portrait, you can easily count and name all of the people in the picture and even guess at their emotions from their facial expressions (Figure 1.2a). Perceptual psychologists have spent decades trying to understand how the visual system works and, even though they can devise optical illusions ${ }^1$ to tease apart some of its principles (Figure 1.3), a complete solution to this puzzle remains elusive (Marr 1982; Wandell 1995; Palmer 1999; Livingstone 2008; Frisby and Stone 2010).

Researchers in computer vision have been developing, in parallel, mathematical techniques for recovering the three-dimensional shape and appearance of objects in imagery. Here, the progress in the last two decades has been rapid. We now have reliable techniques for accurately computing a 3D model of an environment from thousands of partially overlapping photographs (Figure 1.2c). Given a large enough set of views of a particular object or façade, we can create accurate dense 3D surface models using stereo matching (Figure 1.2d). We can even, with moderate success, delineate most of the people and objects in a photograph (Figure 1.2a). However, despite all of these advances, the dream of having a computer explain an image at the same level of detail and causality as a two-year old remains elusive.

Why is vision so difficult? In part, it is because it is an inverse problem, in which we seek to recover some unknowns given insufficient information to fully specify the solution. We must therefore resort to physics-based and probabilistic models, or machine learning from large sets of examples, to disambiguate between potential solutions. However, modeling the visual world in all of its rich complexity is far more difficult than, say, modeling the vocal tract that produces spoken sounds.

The forward models that we use in computer vision are usually developed in physics (radiometry, optics, and sensor design) and in computer graphics. Both of these fields model how objects move and animate, how light reflects off their surfaces, is scattered by the atmosphere, refracted through camera lenses (or human eyes), and finally projected onto a flat (or curved) image plane. While computer graphics are not yet perfect, in many domains, such as rendering a still scene composed of everyday objects or animating extinct creatures such as dinosaurs, the illusion of reality is essentially there.

In computer vision, we are trying to do the inverse, i.e., to describe the world that we see in one or more images and to reconstruct its properties, such as shape, illumination, and color distributions. It is amazing that humans and animals do this so effortlessly, while computer vision algorithms are so error prone. People who have not worked in the field often underestimate the difficulty of the problem. This misperception that vision should be easy dates back to the early days of artificial intelligence (see Section 1.2), when it was initially believed that the cognitive (logic proving and planning) parts of intelligence were intrinsically more difficult than the perceptual components (Boden 2006).

## 计算机代写|计算机视觉代写Computer Vision代考|A brief history

In this section, I provide a brief personal synopsis of the main developments in computer vision over the last fifty years (Figure 1.6) with a focus on advances I find personally interesting and that have stood the test of time. Readers not interested in the provenance of various ideas and the evolution of this field should skip ahead to the book overview in Section 1.3.
1970s. When computer vision first started out in the early $1970 \mathrm{~s}$, it was viewed as the visual perception component of an ambitious agenda to mimic human intelligence and to endow robots with intelligent behavior. At the time, it was believed by some of the early pioneers of artificial intelligence and robotics (at places such as MIT, Stanford, and CMU) that solving the “visual input” problem would be an easy step along the path to solving more difficult problems such as higher-level reasoning and planning. According to one well-known story, in 1966, Marvin Minsky at MIT asked his undergraduate student Gerald Jay Sussman to “spend the summer linking a camera to a computer and getting the computer to describe what it saw” (Boden 2006, p. 781). ${ }^5$ We now know that the problem is slightly more difficult than that. ${ }^6$

What distinguished computer vision from the already existing field of digital image processing (Rosenfeld and Pfaltz 1966; Rosenfeld and Kak 1976) was a desire to recover the three-dimensional

structure of the world from images and to use this as a stepping stone towards full scene understanding. Winston (1975) and Hanson and Riseman (1978) provide two nice collections of classic papers from this early period.

Early attempts at scene understanding involved extracting edges and then inferring the 3D structure of an object or a “blocks world” from the topological structure of the 2D lines (Roberts 1965). Several line labeling algorithms (Figure 1.7a) were developed at that time (Huffman 1971; Clowes 1971; Waltz 1975; Rosenfeld, Hummel, and Zucker 1976; Kanade 1980). Nalwa (1993) gives a nice review of this area. The topic of edge detection was also an active area of research; a nice survey of contemporaneous work can be found in (Davis 1975).

Three-dimensional modeling of non-polyhedral objects was also being studied (Baumgart 1974; Baker 1977). One popular approach used generalized cylinders, i.e., solids of revolution and swept closed curves (Agin and Binford 1976; Nevatia and Binford 1977), often arranged into parts relationships ${ }^7$ (Hinton 1977; Marr 1982) (Figure 1.7c). Fischler and Elschlager (1973) called such elastic arrangements of parts pictorial structures (Figure 1.7b).

A qualitative approach to understanding intensities and shading variations and explaining them by the effects of image formation phenomena, such as surface orientation and shadows, was championed by Barrow and Tenenbaum (1981) in their paper on intrinsic images (Figure 1.7d), along with the related $21 / 2$-D sketch ideas of Marr (1982). This approach has seen periodic revivals, e.g., in the work of Tappen, Freeman, and Adelson (2005) and Barron and Malik (2012).

More quantitative approaches to computer vision were also developed at the time, including the first of many feature-based stereo correspondence algorithms (Figure 1.7e) (Dev 1974; Marr and Poggio 1976, 1979; Barnard and Fischler 1982; Ohta and Kanade 1985; Grimson 1985; Pollard, Mayhew, and Frisby 1985) and intensity-based optical flow algorithms (Figure 1.7f) (Horn and Schunck 1981; Huang 1981; Lucas and Kanade 1981; Nagel 1986). The early work in simultaneously recovering $3 \mathrm{D}$ structure and camera motion (see Chapter 11) also began around this time (Ullman 1979; Longuet-Higgins 1981).

## 计算机代写|计算机视觉代写Computer Vision代考|A brief history

70 年代。当计算机视觉在早期开始时1970 秒，它被视为模仿人类智能并赋予机器人智能行为的雄心勃勃议程的视觉感知组成部分。当时，人工智能和机器人技术的一些早期先驱（在麻省理工学院、斯坦福大学和卡内基梅隆大学）认为，解决“视觉输入”问题将是解决更困难的道路上的一个简单步骤高级推理和规划等问题。根据一个众所周知的故事，1966 年，麻省理工学院的马文·明斯基 (Marvin Minsky) 要求他的本科生杰拉尔德·杰伊·萨斯曼 (Gerald Jay Sussman) “用整个夏天将相机连接到计算机，并让计算机描述它所看到的内容”（Boden 2006，第 781 页） ).5我们现在知道这个问题比那个稍微难一点。6

Barrow 和 Tenenbaum (1981) 在他们关于固有图像的论文（图 1.7d）中提倡采用定性方法来理解强度和阴影变化并通过图像形成现象（例如表面方向和阴影）的影响来解释它们，以及相关的21/2-D 素描 Marr (1982) 的想法。这种方法已经周期性地复兴，例如，在 Tappen、Freeman 和 Adelson（2005 年）以及 Barron 和 Malik（2012 年）的工作中。

## 广义线性模型代考

statistics-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。

## 电子工程代写|计算机视觉代写Computer Vision代考|CPS843

statistics-lab™ 为您的留学生涯保驾护航 在代写计算机视觉Computer Vision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。我们的专家在代写计算机视觉Computer Vision方面经验极为丰富，各种代写计算机视觉Computer Vision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|计算机视觉代写Computer Vision代考|Performance evaluation of algorithms

A systematic evaluation of the algorithms for computer vision has been widely neglected. For a newcomer to computer vision with an engineering background or a general education in natural sciences this is a strange experience. It appears to him/her as if one would present results of measurements without giving error bars or even thinking about possible statistical and systematic errors.

What is the cause of this situation? On the one side, it is certainly true that some problems in computer vision are very hard and that it is even harder to perform a sophisticated error analysis. On the other hand, the computer vision community has ignored the fact to a large extent that any algorithm is only as good as its objective and solid evaluation and verification.

Fortunately, this misconception has been recognized in the meantime and there are serious efforts underway to establish generally accepted rules for the performance analysis of computer vision algorithms [9]. The three major criteria for the performance of computer vision algorithms are:

Successful solution of task. Any practitioner gives this a top priority. But also the designer of an algorithm should define precisely for which task it is suitable and what the limits are.

Accuracy. This includes an analysis of the statistical and systematic errors under carefully defined conditions (such as given signal-tonoise ratio (SNR), etc.).

Speed. Again this is an important criterion for the applicability of an algorithm.

There are different ways to evaluate algorithms according to the forementioned criteria. Ideally this should include three classes of studies:
Analytical studies. This is the mathematically most rigorous way to verify algorithms, check error propagation, and predict catastrophic failures.

## 电子工程代写|计算机视觉代写Computer Vision代考|Electromagnetic waves

Electromagnetic radiation consists of electromagnetic waves carrying energy and propagating through space. Electrical and magnetic fields are alternating with a temporal frequency $\nu$ and a spatial wavelength $\lambda$. The metric units of $v$ and $\lambda$ are cycles per second $\left(\mathrm{s}^{-1}\right)$, and meter $(\mathrm{m})$, respectively. The unit $1 \mathrm{~s}^{-1}$ is also called one hertz $(1 \mathrm{~Hz})$. Wavelength and frequency of waves are related by the speed of light $c$ :
$$c=v \lambda$$
The speed of light depends on the medium through which the electromagnetic wave is propagating. In vacuum, the speed of light has the value $2.9979 \times 10^{8} \mathrm{~m} \mathrm{~s}^{-1}$, which is one of the fundamental physical constants and constitutes the maximum possible speed of any object. The speed of light decreases as it penetrates matter, with slowdown being dependent upon the electromagnetic properties of the medium (see Section 2.5.2).

Photon energy. In addition to electromagnetic theory, radiation can be treated as a flow of particles, discrete packets of energy called photons. One photon travels at the speed of light $c$ and carries the energy
$$e_{p}=h v=\frac{h c}{\lambda}$$
where $h=6.626 \times 10^{-34} \mathrm{~J}$ s is Planck’s constant. Therefore the energy content of radiation is quantized and can only be a multiple of $h v$ for a certain frequency $v$. While the energy per photon is given by Eq. (2.2), the total energy of radiation is given by the number of photons. It was this quantization of radiation that gave birth to the theory of quantum méchanics at the beginning of the twentieth century.

## 电子工程代写|计算机视觉代写Computer Vision代考|Electromagnetic waves

$$c=v \lambda$$

$$e_{p}=h v=\frac{h c}{\lambda}$$

## 广义线性模型代考

statistics-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。

## 电子工程代写|计算机视觉代写Computer Vision代考|CMSC426

statistics-lab™ 为您的留学生涯保驾护航 在代写计算机视觉Computer Vision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。我们的专家在代写计算机视觉Computer Vision方面经验极为丰富，各种代写计算机视觉Computer Vision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|计算机视觉代写Computer Vision代考|Signal processing for computer vision

One-dimensional linear signal processing and system theory is a standard topic in electrical engineering and is covered by many standard textbooks (e.g., [1, 2]). There is a clear trend that the classical signal processing community is moving into multidimensional signals, as indicated, for example, by the new annual international IEEE conference on image processing (ICIP). This can also be seen from some recently published handbooks on this subject. The digital signal processing handbook by Madisetti and Williams [3] includes several chapters that deal with image processing. Likewise the transforms and applications handbook by Poularikas [4] is not restricted to 1-D transforms.

There are, however, only a few monographs that treat signal processing specifically for computer vision and image processing. The monograph by Lim [5] deals with 2-D signal and image processing and tries to transfer the classical techniques for the analysis of time series to 2-D spatial data. Granlund and Knutsson [6] were the first to publish a monograph on signal processing for computer vision and elaborate on a number of novel ideas such as tensorial image processing and normalized convolution that did not have their origin in classical signal processing.

Time series are 1-D, signals in computer vision are of higher dimension. They are not restricted to digital images, that is, 2-D spatial signals (Chapter 8). Volumetric sampling, image sequences, and hyperspectral imaging all result in 3-D signals, a combination of any of these techniques in even higher-dimensional signals.

How much more complex does signal processing become with increasing dimension? First, there is the explosion in the number of data points. Already a medium resolution volumetric image with $512^{3}$ voxels requires $128 \mathrm{MB}$ if one voxel carries just one byte. Storage of even higher-dimensional data at comparable resolution is thus beyond the capabilities of today’s computers.

## 电子工程代写|计算机视觉代写Computer Vision代考|Pattern recognition for computer vision

The basic goal of signal processing in computer vision is the extraction of “suitable features” for subsequent processing to recognize and classify objects. But what is a suitable feature? This is still less well defined than in other applications of signal processing. Certainly a mathematically well-defined description of local structure as discussed in Section $9.8$ is an important basis. As signals processed in computer vision come from dynamical 3-D scenes, important features also include motion (Chapter 10) and various techniques to infer the depth in scenes including stereo (Section 11.2), shape from shading and photometric stereo, and depth from focus (Section 11.3).

There is little doubt that nonlinear techniques are crucial for feature extraction in computer vision. However, compared to linear filter techniques, these techniques are still in their infancy. There is also no single nonlinear technique but there are a host of such techniques often specifically adapted to a certain purpose [7]. In this volume, we give an overview of the various classes of nonlinear filter techniques (Section 9.4) and focus on a first-order tensor representation of nonlinear filters by combination of linear convolution and nonlinear point operations (Chapter 9.8) and nonlinear diffusion filtering (Chapter 12).
In principle, pattern classification is nothing complex. Take some appropriate features and partition the feature space into classes. Why is it then so difficult for a computer vision system to recognize objects? The basic trouble is related to the fact that the dimensionality of the input space is so large. In principle, it would be possible to use the image itself as the input for a classification task, but no real-world classification technique-be it statistical, neuronal, or fuzzy-would be able to handle such high-dimensional feature spaces. Therefore, the need arises to extract features and to use them for classification.

Unfortunately, techniques for feature selection have very often been neglected in computer vision. They have not been developed to the same degree of sophistication as classification, where it is meanwhile well understood that the different techniques, especially statistical and neural techniques, can been considered under a unified view [8].

This book focuses in part on some more advanced feature-extraction techniques. An important role in this aspect is played by morphological operators (Chapter 14) because they manipulate the shape of objects in images. Fuzzy image processing (Chapter 16) contributes a tool to handle vague data and information.

Object recognition can be performed only if it is possible to represent the knowledge in an appropriate way. In simple cases the knowledge can just rest in simple models. Probabilistic modeling in computer vision is discussed in Chapter 15. In more complex cases this is not sufficient.

## 广义线性模型代考

statistics-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。

## 电子工程代写|计算机视觉代写Computer Vision代考|CS763

statistics-lab™ 为您的留学生涯保驾护航 在代写计算机视觉Computer Vision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。我们的专家在代写计算机视觉Computer Vision方面经验极为丰富，各种代写计算机视觉Computer Vision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|计算机视觉代写Computer Vision代考|Components of a vision system

Computer vision is a complex subject. As such it is helpful to divide it into its various components or function modules. On this level, it is also much easier to compare a technical system with a biological system. In this sense, the basic common functionality of biological and machine vision includes the following components (see also Table 1.1):
Radiation source. If no radiation is emitted from the scene or the object of interest, nothing can be observed or processed. Thus appropriate illumination is necessary for objects that are themselves not radiant.

Camera. The “camera” collects the radiation received from the object in such a way that the radiation’s origins can be pinpointed. In the simplest case this is just an optical lens. But it could also be a completely different system, for example, an imaging optical spectrometer, an x-ray tomograph, or a microwave dish.

Sensor. The sensor converts the received radiative flux density into a suitable signal for further processing. For an imaging system normally a 2-D array of sensors is required to capture the spatial distribution of the radiation. With an appropriate scanning system in some cases a single sensor or a row of sensors could be sufficient.

Processing unit. It processes the incoming, generally higher-dimensional data, extracting suitable features that can be used to measure object properties and categorize them into classes. Another important component is a memory system to collect and store knowledge about the scene, including mechanisms to delete unimportant things.

Actors. Actors react to the result of the visual observation. They become an integral part of the vision system when the vision system is actively responding to the observation by, for example, tracking an object of interest or by using a vision-guided navigation (active vision, perception action cycle).

## 电子工程代写|计算机视觉代写Computer Vision代考|Imaging systems

Imaging systems cover all processes involved in the formation of an image from objects and the sensors that convert radiation into electric signals, and further into digital signals that can be processed by a computer. Generally the goal is to attain a signal from an object in such a form that we know where it is (geometry), and what it is or what properties it has.

It is important to note that the type of answer we receive from these two implicit questions depends on the purpose of the vision system. The answer could be of either a qualitative or a quantitative nature. For some applications it could be sufficient to obtain a qualitative answer like “there is a car on the left coming towards you.” The “what” and “where” questions can thus cover the entire range from “there is something,” a specification of the object in the form of a class, to a detailed quantitative description of various properties of the objects of interest.

The relation that links the object property to the signal measured by an imaging system is a complex chain of processes (Fig. 1.1). Interaction of the radiation with the object (possibly using an appropriate illumination system) causes the object to emit radiation. A portion (usually only a very small part) of the emitted radiative energy is collected by the optical system and perceived as an irradiance (radiative energy/area). A sensor (or rather an array of sensors) converts the received radiation into an electrical signal that is subsequently sampled and digitized to form a digital image as an array of digital numbers.

Only direct imaging systems provide a direct point-to-point correspondence between points of the objects in the 3-D world and at the image plane. Indirect imaging systems also give a spatially distributed irradiance but with no such one-to-one relation. Generation of an image requires reconstruction of the object from the perceived irradiance. Examples of such imaging techniques include radar imaging, various techniques for spectral imaging, acoustic imaging, tomographic imaging, and magnetic resonance imaging.

## 广义线性模型代考

statistics-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。

## 统计代写|计算机视觉作业代写Computer Vision代考|Preventing Security Breach in Social Media

statistics-lab™ 为您的留学生涯保驾护航 在代写计算机视觉Computer Vision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。我们的专家在代写计算机视觉Computer Vision代写方面经验极为丰富，各种代写计算机视觉Computer Vision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

## 统计代写|计算机视觉作业代写Computer Vision代考|Threats and Prevention Techniques

Social media networks such as Facebook, Instagram, Twitter, and WhatsApp have become the prime source of sharing personal information, thoughts, news, photos, videos, messages with our friends on a daily basis. While using social media in the present it seems very easy to share data within seconds but before 1970 it was not that easy. In 1971, the first email was sent between two computers which were sitting next to each other; it was

the beginning of the era of sharing information within two different devices in real time. Bulletin Board System was introduced in 1987 to share data over phone lines all around the world. The first social media site namely Geocities.ws was founded by David Bohnett and John Razner in November 1994 which was called Beverly Hills Internet; it was further occupied by Yahoo in $1999 .$

Social media networks deal with a huge amount of data that need to be provided to the productive analyzing companies in a secure way keeping in mind the difference between the private and the public data of the users. Handling that much becomes difficult which leads to breach of user privacy. These breaches would also lead to physical damage as social media requires the permission of accessing contact, sharing location, etc., which is the credential data of a victim. Social media users User Interface Description Language to develop multilingual, multi-platform, and multimodal user interfaces so that the user could operate in the social network from anywhere and any type of device easily.

## 统计代写|计算机视觉作业代写Computer Vision代考|Related Research Work

William et al. [1] discussed the behavior of different types of users and groups in a social media website and describe how these types of activity affect privacy in different social media applications. Hashimoto et al. [2] discussed the benefits of using social media sites in a safe way. The author showcases the key components affecting the user’s privacy in social media and how to overcome those components without changing the functionality of the application or keeping the information sharing concept as it is. Kumar et al. [3] described the privacy issues, security issues, identity theft, profiling risk present in these types of applications. It also discusses different types of attacks which can be performed on the social network to hack the privacy of the user which also includes the new attack strategy occupied by the hackers. It also provides us with the step to prevent the above attacks. Fire et al. [4] discussed a detailed review of the different privacy and security risks, which threaten the well-being of online social network users in general, and children in particular. It also presents an overview of existing solutions that can provide better protection, security, and privacy for online social network users. Senthil Kumar et al. [5] describe social media as an essential part of humans in day-to-day life. While enjoying the social media platforms user need to understand which information is needed to be kept private and undisclosed so that no one could use that information in the wrong way. Kumari and Singh [6] described privacy as the main concern in social media sites such as Facebook and Twitter. The main purpose of these sites is to provide the user with the facility to share information. Lack of attention on these sites can lead to a big violation to overcome these various methods for securing the data being covered. Kumar et al. [7] discuss how the prevention of different social media vulnerabilities can be integrated into the design of the social sites to facilitate interaction while enhancing privacy and security. Kumar et al. [8] discussed the present privacy and security issues of the social network and also discussed each of the popular sites with some of the precaution which needs to be taken up.

## 统计代写|计算机视觉作业代写Computer Vision代考|Importance of Social Media

Usage of social media websites has increased to the extent that almost every person on earth is using the social network to share, communicate, and discuss different things with colleagues, family members, friends, etc., in a formal or an informal way. Social networking sites are performing well on the internet by receiving more than 10,000 hits per second and this is because these sites have many advantages which help their users daily. Here are a few of them:

• Communication
The main role of social media is it helps its audience to communicate with anyone all around the world at no cost which keeps them connected and up to date with other people, society, organizations, etc.
• Exchanging Data
Information sharing is very easy and quick, we can share our information with a single person at a time or we have the choice to share it with multiple persons at a time using groups.
• Discloser of Inactivity
It also helps in the discloser doing inappropriate activities like harming animals. This could be a great advantage for police to detect the person doing inactivity across the nation.
Helps in creating your community of people through which you can earn by sharing your links, blog posts. On YouTube we can share our videos and earn per view, and on Facebook we can share our posts, photos, and videos on our pages.
Online advertising has become the greatest source of getting the right customer but there are very few sites which provide effective advertising. So, the role of social network sites provides the facility to promote our business with ads in a cost-effective way.
• Doubt Solving
As a student or professional we come across difficult questions to answer. The role of social media sites like Reddit and Quora helps users to get answers to their questions in any field.
• Entertainment
These sites also help users to refresh themselves through different sources of entertainment such as videos, jokes, and photos. Which are being posted by the other you’re and you too have the right to present you entertaining stuff on the media sites and become popular through it.
• Quick Response
The information posted on social media spreads quickly which could be a good point for correct news but it also helps the rumors to spread quickly. This reach also depends on the number of members associated with the person posting the content. The quicker the information spread, the quicker we will get a response.

## 统计代写|计算机视觉作业代写Computer Vision代考|Importance of Social Media

• 交流
社交媒体的主要作用是帮助其受众免费与世界各地的任何人交流，使他们与其他人、社会、组织等保持联系并保持最新。
• 交换数据
信息共享非常简单快捷，我们可以一次与一个人共享我们的信息，也可以选择使用群组一次与多个人共享。
• 不活动的披露者
它还有助于披露者进行不适当的活动，例如伤害动物。这对于警方在全国范围内发现不活动的人来说可能是一个很大的优势。
• 增强业务
• 具有成本效益的广告
在线广告已成为获得合适客户的最大来源，但提供有效广告的网站很少。因此，社交网站的作用为以具有成本效益的方式通过广告宣传我们的业务提供了便利。
• 解决疑问
作为学生或专业人士，我们会遇到难以回答的问题。Reddit 和 Quora 等社交媒体网站的作用是帮助用户在任何领域获得问题的答案。
• 娱乐
这些网站还通过视频、笑话和照片等不同的娱乐来源帮助用户恢复活力。其他人正在发布哪些内容，您也有权在媒体网站上向您展示娱乐内容并通过它变得流行。
• 快速反应
社交媒体上发布的信息传播速度很快，这可能是正确新闻的一个好点，但它也有助于谣言迅速传播。此范围还取决于与发布内容的人相关联的成员数量。信息传播得越快，我们就会越快得到回应。

## 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。

## 统计代写|计算机视觉作业代写Computer Vision代考|Use of Robotics in Real-Time Applications

statistics-lab™ 为您的留学生涯保驾护航 在代写计算机视觉Computer Vision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。我们的专家在代写计算机视觉Computer Vision代写方面经验极为丰富，各种代写计算机视觉Computer Vision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

## 统计代写|计算机视觉作业代写Computer Vision代考|Mukesh Carpenter

In today’s world, everyone is aware of a huge range of thinking to expand robotic technology after its use in industry technology. It is the part of the engineering and technology that comprise electrical engineering and information technology. This branch manages the structure, development, usage to control robots, tangible input, and data preparing [1]. Those robots are intended to be utilized for many reasons; however, these will be utilized in delicate conditions such as bomb recognition, deactivating procedure of different bombs, and so on. Robots would make any frame; however, a significant number of these robots possess human behavior and nature.

These robots will look like humans and have the ability to walk, discourse, and moreover all of the things a human can do $[1,2,10-36]$. The greater part of these robots is propelled commonly and is called bio-enlivened robots.
There are a few kinds of robots as follows:
(i) Explained
Components in these robots are rotating connections and the scope is from 2 to 10 connections. Here, the arm is associated with a revolving connection, and each connection is called the pivot which will give scope for developments $[3,4]$.
(ii) Cartesian
These are otherwise called gantry robots. There are three connections that utilize the Cartesian framework: $a, b, c$. These types of robots are furnished with appended wrists to give rotatory movement.

(iii) Round and hollow
These sorts of robots have no less than one revolving connection and one kaleidoscopic joint which are utilized to interface the connections. The utilization of rotatory connection is to pivot with the hub and kaleidoscopic connections are used to give straight movement.
(iv) Polar
These are otherwise called round robots. Here, the arm is associated with a bending connection and has a mix of two revolving connections and one straight joint. These robots are mostly utilized in gathering uses. Its arm is tube-shaped in the plan. They have the two connections at an equal distance which are utilized to give consistency in one chosen plane.
(v) Delta
The formation of these robots resembles arachnid. They work by connecting a trapezium that is associated with the base $[4,6]$. The trapezium moves in an arch-formed working region. These are utilized for the most part in sustenance and electrical ventures. The World Technology Evaluation Center is an American association that surveys the condition of innovations around the globe. Their investigations can be subsidized by different American government bodies, for example, the National Aeronautics and Space Administration, Defense Advanced Research Projects Agency, and so on. In our endeavors to fire up a mechanical technology organization, I inspected one of their reports distributed in 2006, titled “Universal Assessment of Research and Development in Robotics.” This report was composed by researchers gaining practical experience in the field [6-8,39-53]. They visited and talked with researchers from organizations, and the research focuses on various nations: the USA, Japan, South Korea, Australia, and Europe [9]. The report portrays the present condition of mechanical technology, contrasts the USA and whatever remains of the world (as per the report), and talks about future difficulties in applying autonomy, which is of unique enthusiasm tome:

• Mechanical vehicles, space apply autonomy, humanoids.
• Apply autonomy is that part of a building that manages origination, structure, task, and assembling of robots. There was a creator named Isaac Asimov. He said that he was the main individual to give a name to apply autonomy in a short story made in the $1940 \mathrm{~s}$. In that story, Isaac proposed three standards about how to direct these kinds of mechanical machines. Later, these three standards were given the name of Isaac’s three laws of robotics [10]. These three laws are as follows:
• Robots will never hurt individuals.
• Robots will adhere to directions given by people with infringing upon law one.
• Robots will secure themselves without defying different guidelines.

## 统计代写|计算机视觉作业代写Computer Vision代考|Related Work

Greczek et al. [1] discussed how to standardize and replace the computer technologies by socially assistive robotics as it has the potential to do what has the edge of knowing in tangible context, plan to structure mechanical frameworks that are convincing, help youngsters in accomplishing instructive objectives. Rischet [2] discussed the progression in apply autonomy innovation. Unmanned rural apply autonomy is broadly utilized in exactness agribusiness. Architects outfitted with mechanical technology information are exceedingly requested by the present high effectiveness, high-creating rural industry. Shin et al. [3] investigated if the students could learn this core computer science concept while enjoying themselves in the robotic context. A visual questionnaire was developed based upon the combined Bloom and SOLO taxonomies, although it proved to be difficult to construct a questionnaire appropriate for a young student. Jovanović [4] discussed that the structure of present-day automated gadgets faces various necessities and impediments which are identified with enhancement and power. Therefore, these stringent necessities have caused enhancements in many building territories and led to the improvement of new streamlining techniques which better handle new complex items intended for application in modern robots. Ayushnarula et al. [5] discussed that step-by-step instructions to do work-savvy robots in surgeries have been totally determined by the kind of medical procedure. Similarly, the main job of creating savvy robots is as of now being taken up by the private area. Bhattacharyya [6] discussed the utilization of electroencephalogram (EEG) signals for controlling in the field of mechanical autonomy and utilizing a reasonable mapping process known as a brain-computer interface. Different deterioration strategies of the EEG motion for highlight extraction have been proposed by numerous analysts. Joshi et al. [8] discussed the neural circuits that control getting a handle on and perform related visual handling have been examined broadly in macaque monkeys. We are building up a computational model of this framework so as to comprehend its capacity and investigate applications to mechanical technology. Subramanian et al. [9] discussed numerous mechanical spots on the planet from multiple points of view to recognize essential jobs in numerous businesses for some reasons. Liang [10] demonstrated that show-based, probabilistic reverse fortification learning Intelligent Robotics Lab Facilities (IRL) is attainable in high-measurement, state-activity spaces with just a solitary master exhibition. By executing the IRL max edge calculation with a probabilistic model-based fortification learning calculation named PILCO, we can join the calculations to make the IRL/PILCO calculation, which is equipped for replicating master directions by picking appropriate highlights.

## 统计代写|计算机视觉作业代写Computer Vision代考|Current Challenging Issues

There are various challenging issues faced by robotics in the recent past. Some of them are listed below:

• New Materials, Creation Techniques
Apparatuses, engines, and actuators are central to the present robots. So, huge works are being carried out with fake muscles, delicate mechanical autonomy, and

techniques that will help build up the upcoming age of self-ruling robots to do many functions at the same time [13].

• Making Eco-Friendly-Enlivened Robots
Naturally enlivened robotics are doing their work progressively basic in automations autonomy labs. The primary aim is to create robots that function more like the effective frameworks found in the atmosphere. The investigation says that the significant difficulties required with this territory have remained, to a great extent, unaltered for a long time-high power cells to coordinate metabolic transformation, muscle-like actuators, self-mending parts that’s been used in robotics, independence in every condition, human beings-like recognition, with calculation while thinking accordingly. Materials which are being used together in detection, activation, calculation, with correspondence should be created and discussed and connected with each other. This advancement will prompt robots with highlights, for example, physical support, force decrease, sway security, physiological calculation, and versatility [15].
• Good Resources in Force
Improvising the battery life is a noteworthy case in automatons and portable robotics, in particular. Fortunately, expanded selection in these frameworks is prompting unused or best battery advancements which are moderate, protected, and enduring. The task is given and completed so as to make the segments of a robot more efficient. So, the examination refers to robots that need to work remotely in unstructured situations and will in the long run concentrate vitality for some lightening, oscillations, and mechanical development $[16,48]$.
• Connections in Robots in Swarms
Discernment activity circles are basic to making self-ruling robotic work in unpatented conditions. Robot swarms need correspondence capacity to insert in this input circle. Consequently, recognition activity openness circles are of utmost importance to structuring robot swarms. There are no efficient methodologies for doing this crosswise over expansive gatherings [17].
• Navigate Untracked Environment
Step-by-step instructions to reason about new ideas and their semantic portrayals and find new articles or classes on the earth through learning and dynamic associations [18]. Per the examination:
For route, the great test is to deal with disappointments and having the capacity to adjust, learn, and recoup. For investigation, it is building up the natural capacities to make and perceive new disclosures. From a framework viewpoint, this requires the physical heartiness to withstand cruel, alterable conditions, harsh dealing with, and complex control. The robots need huge dimensions of self-rule prompting complex self-checking, self-reconfiguration, and fix with the end goal that there is no single purpose of complete disappointment but instead elegant framework debasement. Whenever possible, arrangements need to include control of different heterogeneous robots; adaptively organize, interface, and utilize various resources; and offer data from numerous information wellsprings of variable unwavering quality and exactness.

## 统计代写|计算机视觉作业代写Computer Vision代考|Mukesh Carpenter

(i) 解释

(ii) 笛卡尔

(iii) 圆形和中空

(iv) Polar

(v) 三角洲

• 机械车辆，空间应用自治，人形机器人。
• 应用自治是管理机器人的起源、结构、任务和组装的建筑物的一部分。有一位名叫艾萨克·阿西莫夫的创造者。他说，他是在一个短篇小说中命名以应用自治权的主要人物。1940 s. 在那个故事中，艾萨克提出了三个关于如何指导这些机械机器的标准。后来，这三个标准被命名为艾萨克机器人三定律[10]。这三项法律如下：
• 机器人永远不会伤害个人。
• 机器人将遵守违反第一条法律的人给出的指示。
• 机器人将在不违反不同准则的情况下保护自己。

## 统计代写|计算机视觉作业代写Computer Vision代考|Current Challenging Issues

• 新材料、创造技术
设备、引擎和执行器是目前机器人的核心。因此，大量的工作正在用假肌肉、精密的机械自主性和

• 制作环保型机器人
自然活跃的机器人技术正在自动化自主实验室中逐步开展基础工作。主要目标是创造功能更像大气中有效框架的机器人。调查表明，该领域所需的重大困难在很长一段时间内一直没有改变——用于协调代谢转化的高功率电池、类似肌肉的致动器、用于机器人技术的自我修复部件、独立性每一个条件，人类一样的识别，一边计算一边据此思考。在检测、激活、计算中一起使用的材料，应创建和讨论并相互连接。这一进步将提示机器人具有亮点，例如，物理支持，力量减少，
• 有效的良好资源
延长电池寿命是自动机和便携式机器人的一个值得注意的案例，尤其是。幸运的是，这些框架中的扩展选择促使未使用的或最佳的电池改进，这些改进是适度的、受保护的和持久的。给出并完成任务是为了使机器人的各个部分更有效率。所以，考试是指需要在非结构化的情况下远程工作的机器人，从长远来看，会集中精力进行一些闪电、振荡和机械开发[16,48].
• 群中机器人的连接 辨别
活动圈是使自主机器人在非专利条件下工作的基础。机器人群需要对应容量才能插入到这个输入圈中。因此，识别活动开放圈对于构建机器人群至关重要。没有有效的方法可以在广泛的聚会上进行交叉[17]。
• 导航未跟踪环境
逐步说明推理新思想及其语义描述，并通过学习和动态关联在地球上找到新文章或新课程 [18]。根据考试：
对路线而言，最大的考验是应对挫折，并具备调整、学习和弥补的能力。对于调查，它正在建立自然能力，以进行和感知新的披露。从框架的角度来看，这需要身体的热忱来承受残酷、多变的条件、苛刻的处理和复杂的控制。机器人需要巨大的自我规则维度来促使复杂的自我检查、自我重新配置，并以最终目标进行修复，即没有完全失望的单一目的，而是优雅的框架贬低。在可能的情况下，安排需要包括对不同异构机器人的控制；自适应地组织、接口和利用各种资源；并提供来自众多信息源泉的数据，这些信息源泉的质量和准确性各不相同。

## 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。

## 统计代写|计算机视觉作业代写Computer Vision代考|An Overview of Security Issues of Internet of Things

statistics-lab™ 为您的留学生涯保驾护航 在代写计算机视觉Computer Vision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。我们的专家在代写计算机视觉Computer Vision代写方面经验极为丰富，各种代写计算机视觉Computer Vision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

## 统计代写|计算机视觉作业代写Computer Vision代考|Smart IoT Devices

An IoT device, also referred to as a smart device, can be anything such as home appliances, medical healthcare devices, vehicles, homes, workshops, factories, and cities. Anything can be attached with a microprocessor and sensors, providing data about the real world and transferring those data through the internet. There are many types of sensors (e.g., temperature, humidity, pressure, distance, light, and motion) which are embedded in the device. An IoT device can be configured to interact with other IoT devices and computers. These devices communicate through various means (e.g., broadband, cellular data, and Wi-Fi) $[5,6]$. Power supply to these IoT devices plays an important role in mobility or rigidity. For example, a small device which is capable of working without a constant wired power supply can be very handy. Such devices are generally preferred by consumers because they are more convenient. Other types of bigger things in IoT include healthcare devices (e.g., CT scanner, monitor), buildings, and cities, which are rigid and generally have a constant wired power supply. Also, there are things which constantly move and also have a wired power supply such as cars, bikes, and airplanes [5,7]. IoT devices can also be classified whether it is a logical/physical or an IP-enabled/non-IP object. Some of the characteristics of an IoT device are the ability to sense, actuate, and control the energy/power and its connection with the physical world, mobility, and connectivity. Some devices are required to be fast and robust [40-42]. Some are required to be precise, while some are required to be long-lasting. Some devices are provided with external security factors (cases, covers, and triggers), while others are totally exposed. Some examples of IoT devices are as follows:

• Wearable Devices
Fitness bands like Google Home [18] and smartwatches like Apple watch [19] and Samsung Galaxy Gear [20].
• Amazon Echo
It is a hands-free speaker which is connected to a cloud-based voice service [21].
• Philips Hue
It is a smart home lighting system which can be controlled remotely and can sense time and day to adjust lights accordingly [22] (Table 3.1).

## 统计代写|计算机视觉作业代写Computer Vision代考|Major Security Issues of IoT Devices

Security in IoT devices includes protecting information and data, hardware components, and services of the device from unauthorized access. Both the data and information stored in the device and those in transit should be protected $[16,18]$. The major problems with IoT devices are identified as follows:

• Data Integrity
The integrity of data is defined as the assurance of maintaining data accuracy and consistency throughout the storage lifecycle [23].
• System Security
This issue mainly focuses on the overall security of IoT systems to detect various security issues to design various security frameworks and offer appropriate security guidelines to maintain the security of a network.
• Authorization
The process of granting privilege and specifying access rights is known as authorization [24,25].
• Application Security
This security works for the application to manage security challenges or issues as per situation constraints. In general, security evaluation at the application level prevents data hijacking within the app such as hardware or software that minimizes security vulnerabilities.
• Data Confidentiality
The practice of keeping private data secret is known as data confidentiality [23-25].
• System Vulnerabilities
A lot of work is done by researchers in software vulnerability. Various IoT devices have low-quality software susceptibility to different types of vulnerabilities which are common in the early 2000 and late 1990 s. These devices are vulnerable to weak usage of cryptography, authentication, deployment issue, system software (s/s) exploits, and so on.
• Network Security
This security handles communication attacks on the data which can be transmitted between servers and IoT devices.
• Lack of Common Standard
There are various standards for IoT device-manufacturing companies. Therefore, it becomes a major challenging issue to differentiate between authorized and non-authorized devices connected to the internet.

This defines some fundamental problems in IoT devices. The user accessing the device and its services should be properly authorized in order to view, modify, or add any kind of data to the device storage. An IoT device should be able to authorize the person to access the device. Hence, access and authorization control become necessary factors for establishing a secure connection between multiple devices and their services. Privacy protection is an imperative issue in IoT gadgets and administration because of the universal character of the IoT condition [10,17,19]. Elements are associated, and information is conveyed and transmitted over the web, making client protection a delicate subject in many research works. Protection in information accumulation, just as information sharing and its management, and information security matters stay important issues to be updated.

## 统计代写|计算机视觉作业代写Computer Vision代考|Classification of Intruders

Intruders can be individuals, a group of people, or an agency, and the people may belong to an internal or external area. An internal intruder has proper authorization and access but has malicious intents. An external intruder is a person who does not have authorization but has malicious intents of harming the system. These intruders can belong to any one of these following categories $[29,34]$ :

• Individuals
Hackers, professionals, or even people not having any prior knowledge of hacking can use available tools and techniques for their malicious intent. It is very common in youngsters who try to use these tools to either achieve fame for themselves or do it just for fun or revenge [34].
• Organized Group of Persons
Groups of people with criminal intents are becoming more and more common over time. These groups are well organized, keep their original identity unknown, and use an alias as their group name. These groups have some professionals as

well as amateurs who all work together. They do not always have a criminal intent; however, it is important to stop such groups from flourishing. These groups are sufficiently funded and very capable in terms of expertise and resources [34].

• Intelligence Agencies
These are intelligence agencies which are run by government agencies in most of the countries. They constantly make efforts to probe other country’s military networks and systems. To accomplish these tasks, many experts are working together. People of this group have all the latest technologies available to them and are funded largely by their respective governments. They are given tasks such as invading other country’s military systems and searching their own country’s network systems to find out possible threats. They use strong surveillance and monitoring and are the biggest threats to networks but are treated as prime safeguards for the country [34].

## 统计代写|计算机视觉作业代写Computer Vision代考|Smart IoT Devices

• 可穿戴设备
健身手环，如 Google Home [18] 和智能手表，如 Apple watch [19] 和三星 Galaxy Gear [20]。
• Amazon Echo
它是一个免提扬声器，连接到基于云的语音服务 [21]。
• 飞利浦 Hue
它是一种智能家居照明系统，可以远程控制，并且可以感应时间和日期以相应地调整灯光 [22]（表 3.1）。

## 统计代写|计算机视觉作业代写Computer Vision代考|Major Security Issues of IoT Devices

IoT 设备的安全性包括保护设备的信息和数据、硬件组件和服务免受未经授权的访问。存储在设备中的数据和信息以及传输中的数据和信息都应受到保护[16,18]. 物联网设备的主要问题如下：

• 数据完整性 数据
的完整性被定义为在整个存储生命周期中保持数据准确性和一致性的保证 [23]。
• 系统安全
本期主要关注物联网系统的整体安全性，以检测各种安全问题，设计各种安全框架并提供适当的安全指南，以维护网络的安全。
• 授权
授予特权和指定访问权限的过程称为授权[24,25]。
• 应用程序安全
性 此安全性适用于应用程序，以根据情况限制管理安全挑战或问题。一般来说，应用程序级别的安全评估可防止应用程序内的数据劫持，例如将安全漏洞降至最低的硬件或软件。
• 数据机密
性 将私人数据保密的做法称为数据机密性 [23-25]。
• 系统漏洞
研究人员在软件漏洞方面做了很多工作。各种物联网设备对不同类型的漏洞具有低质量的软件敏感性，这些漏洞在 2000 年初和 1990 年代后期很常见。这些设备容易受到密码学、身份验证、部署问题、系统软件 (s/s) 漏洞利用等的弱使用。
• 网络安全
此安全处理对可以在服务器和物联网设备之间传输的数据的通信攻击。
• 缺乏通用标准
物联网设备制造公司有各种标准。因此，区分连接到互联网的授权和非授权设备成为一个主要的挑战问题。

## 统计代写|计算机视觉作业代写Computer Vision代考|Classification of Intruders

• 个人
黑客、专业人士，甚至没有任何黑客先验知识的人都可以使用可用的工具和技术来实现他们的恶意意图。在试图使用这些工具为自己成名或只是为了好玩或报复的年轻人中，这是很常见的 [34]。
• 有组织
的人群 有犯罪意图的人群随着时间的推移变得越来越普遍。这些组组织良好，保持其原始身份未知，并使用别名作为组名。这些团体有一些专业人士

• 情报机构
这些是由大多数国家的政府机构管理的情报机构。他们不断努力探查他国的军事网络和系统。为了完成这些任务，许多专家正在共同努力。这个群体的人拥有所有可用的最新技术，并且主要由各自政府资助。他们被赋予入侵他国军事系统和搜索本国网络系统以找出可能的威胁等任务。他们使用强大的监视和监控，是对网络的最大威胁，但被视为国家的主要保障措施[34]。

## 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。

## 统计代写|计算机视觉作业代写Computer Vision代考|Interplay between IoE and IoT

statistics-lab™ 为您的留学生涯保驾护航 在代写计算机视觉Computer Vision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。我们的专家在代写计算机视觉Computer Vision代写方面经验极为丰富，各种代写计算机视觉Computer Vision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

## 统计代写|计算机视觉作业代写Computer Vision代考|France: IoE Smart City Pilot

Cisco is joining forces with the city of Nice, France, and a few nearby and other industry accomplices to construct a smart city for additional development through the introduction of the effects of IoE for urban communities. The undertaking’s fundamental goals are to test and approve an IP-empowered innovation design and financial model, just as to decide the social advantages of IoE. The task depends on a shared stage intended to be more adaptable, granular, and versatile to create metropolitan working frameworks. The shared stage is proposed to make it simpler to set up the new associations that are basic for Nice to turn into a smart city. Also, the undertaking will fill in as an impetus for joining key revelations from this and other smart city activities. The aim is to share what Nice has realized with other hopeful urban areas so that they can make their own smart city structure. The undertaking incorporates four city benefits that can quickly show the advantages and estimation of IoE for the two inhabitants and city authority. As these arrangements are actualized, Cisco and the city of Nice are surveying how accumulated information can be utilized to make data setting explicit and helpful across various administrations. For example, information caught by sensors for traffic designs can help to impart traffic signals automatically [10-19]. The ramifications of information “crossfertilization” and cross-joint effort go past mechanical possibility because they additionally sway the choices of city supervisors, cross-departmental coordinated effort, and back-office activities.

## 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。