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• 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|三维成像代写Three-Dimensional Imaging代考|Image Depth

As mentioned above, the integral imaging system has viewing parameters which represent the quality of a 3-D image. The image depth is defined as the thickness of the integrated image. Figure $3.2$ explains the image depth in the integral imaging system. The integrated image is thrown by each elemental lens; so, the reconstructed images of the integral imaging system are located around the central depth plane which is determined by the lens law. The position of the central depth plane can be obtained by [18]
$$l=\frac{f g}{g-f},$$
where $l$ is the distance between the central depth plane and the lens array, $f$ is the focal length of lens array, and $g$ is the gap between the lens array and the display panel as shown in Fig. 3.2. When $g$ is bigger than $f, l$ has a positive value. This positive value means the central depth plane is located in front of the lens array and the integrated image is displayed in the real mode. On the other hand, when $g$ is smaller than $f, l$ has a negative value and the integrated image and the central depth plane are located behind the lens array, which is the virtual mode.

Figure $3.3$ shows an example of the integrated images and their elemental images which are in the real mode and the virtual mode, respectively. The character “A” shown in Fig. 3.3(a) is located at $80 \mathrm{~mm}$ in front of the lens array while that in Fig. $3.3(\mathrm{c})$ is at $80 \mathrm{~mm}$ behind the lens array.

## 电子工程代写|三维成像代写Three-Dimensional Imaging代考|A System to Extend Image Depth

There are several methods $[9,11,13,17]$ proposed to improve the image depth without the degradation of the other viewing parameters. Such methods are the multiple integral imaging devices method [17], the moving lens array method [9], the optical path controller method [11], and so on. In this section, we introduce a method using two integral imaging devices to improve the image depth.

It is easy to assemble a system using two integral imaging devices by using a beam splitter. Figure $3.4$ shows the basic concept of the system to extend the image depth. As shown in Fig. 3.4, the central depth plane of device 2 is located in front of that of device 1 at a certain distance; then, the marginal image depth of the system is extended about twice without any degradation.
Figure $3.5$ shows the experimental results of the system using two integral imaging devices. The specification of the integral imaging system for the experiments is the same as that of the system used in Fig. 3.3. Figure 3.5(a) shows images of a conventional integral imaging system where the gap is adjusted to make the central depth plane located at $80 \mathrm{~mm}$. The integration plane of the character “A” is located at $80 \mathrm{~mm}$, while that of the character “B” is at $160 \mathrm{~mm}$. Since the longitudinal position of the character “B” is too far from the central depth plane, the image is severely broken. Figure $3.5(\mathrm{~b})$ shows images of the system using two integral imaging devices. The character “A” is displayed on integral imaging device 1 where the central depth plane is set to be located at $80 \mathrm{~mm}$; while the character “B” is reconstructed by integral imaging device 2 where the central depth plane is located at $160 \mathrm{~mm}$. As shown in Fig. 3.5(b) both of the images can be observed clearly, so that improved image depth is achieved by the system using two integral imaging devices.

The system, using two integral imaging devices, can be assembled easily without any mechanical moving parts and any complicated optical instruments. Ease of assembly is the main merit of this method. The system, however, is bulky and this burdensomeness worsens when the system uses more than three integral imaging devices because the system needs a complex optical beam combiner.

# 三维成像代考

## 电子工程代写|三维成像代写Three-Dimensional Imaging代考|Image Depth

$$l=\frac{f g}{g-f},$$

## 有限元方法代写

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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。

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