# 线性代数网课代修|机器学习代写 machine learning代考|LSML22

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• 数值分析
• 高等线性代数
• 矩阵论
• 优化理论
• 线性规划
• 逼近论

## 线性代数作业代写linear algebra代考|IMAGE ENHANCEMENT

Image smoothing eliminates distortion or other small disturbances in the whole image that mean the edges are distorted and iteratively reduces noise. The preferred images are entered, and analyzed with some filters. As seen in Figure 3.2, a median, Gaussian, and Gaber filters were added in the pre-processing stage and the current best solution was updated and a new one was proposed. A median filter, a nonlinear $3 \times 3$ scale feature [Shah et al., 2020], is used to improve the image by minimizing impulses.
$g(x, y)=\frac{1}{2 \pi \sigma^{2}} \exp \left[\frac{-\left(x^{2}+y^{2}\right)}{2 \sigma^{2}}\right] \quad$ Equation 3.1: $2 D$ Gaussian filter
The Gaussian filter is also useful for eliminating high-frequency elements to eliminate fluctuations in an image. This means that medfilt 2 is used to reduce noise only. This low-pass filter eliminates turbulence and increases the smoothness and the exact strength of the surface. A standard deviation of the Gaussian distribution is the distance of the axes from the center and $y$ of the axis from the center of the axis [Tabish et al., 2017]. After pre-processing, the processed image is segmented by a watershed segmentation. This image displays the identified nodes of cancer.

The Gaber function is an image evaluation linear filter that is useful now. The Gaussian and harmonic functionalities can be used. It also increases compression between surrounding areas of objects such as nodules. Equation $3.2$ shows the expression of the 2D Gabor filter [Albu et al., 2019]. Here, in this equation, $\lambda$ is the wavelength; for the orientation, $\theta$ is used from standard to parallel slices; $\varphi$ is the phase offset; $\sigma$ is standard deviation; $\gamma$ is the ratio of spatial aspect. Other characteristics,including surface areas, perimeters, and eccentricity, as well as other features like centroid, diameter, and main intensity pixels, have been developed during the cancer detection development stage. Since then, the features have been extracted and the cancer node accurately evaluated. It’s not apparent what’s benign or malignant thereafter.

## 线性代数作业代写linear algebra代考|IMAGE SEGMENTATION

The best approach to isolate the grayscale information into target grey data is by splitting the threshold segmentation, i.e. local or global. The most widely used is the Otsu algorithm. An initial seed value that merges all identical pixels outside the badge is initially applied. The value is that it usually distinguishes from the related characteristics and reliably gives better data, although it also produces noise or distortions. The benefit is that usually the same characteristics are separated from the related areas and offer improved segmentation output information and the drawback is that computing resources are costlier, which contributes to severe noise or regular spotting. Pre-filtered images are translated into another format, which partially removes the complexity of the front pixel mostly on edges. The process for lung fragmentation, goes through several phases while the strategies for lung segmentation go through various steps like pre-processing, implementation of median filter, Gaussian filter, then the seeding of the Gabor function and new processes are begun, as seen in Figures 3.4(a), 3.4(b), 3.4(c), and 3.4(d), to separate the lungs from the superior and inferior lobe.

# 计量经济学代写

## 在这种情况下，如何学好线性代数？如何保证线性代数能获得高分呢？

1.1 mark on book

【重点的误解】划重点不是书上粗体，更不是每个定义，线代概念这么多，很多朋友强迫症似的把每个定义整整齐齐用荧光笔标出来，然后整本书都是重点，那期末怎么复习呀。我认为需要标出的重点为

A. 不懂，或是生涩，或是不熟悉的部分。这点很重要，有的定义浅显，但证明方法很奇怪。我会将晦涩的定义，证明方法标出。在看书时，所有例题将答案遮住，自己做，卡住了就说明不熟悉这个例题的方法，也标出。

B. 老师课上总结或强调的部分。这个没啥好讲的，跟着老师走就对了

C. 你自己做题过程中，发现模糊的知识点

1.2 take note

1.3 understand the relation between definitions