Inpaint_Nans Interpolation and Extrapolation of NaN Elements in 2D Arrays

二维图像分形维数计算，包含MATLAB代码，包括主函数、盒子数计算、分形维数计算。

这款 Matlab 工具助力模拟 2D 桁架结构，计算并呈现关键结果，例如节点位移和杆件受力。

The PlotClusters function is used for visualizing clustering data, such as the output from k-means, in 2D or 3D. The inputs include: Data: An m×d matrix, where m is the number of data points and d is the number of dimensions. IDX: An m×1 vector that associates each data point with a cluster. Optional inputs:- Centers: A c×d matrix representing the c cluster centers. If not provided, the function will compute them.- Colors: A c×3 matrix generated using the hsv command, where the number of colors must be at least equal to the number of clusters. The function uses plot or plot3 for visualizing the clusters in 2D or 3D, respectively. Note: This function has been tested only on MATLAB version 2008a on Windows but should work for all versions.

在Matlab开发环境中，通过SCALEBAR函数可以在二维轴上创建可定制的比例尺。此函数允许用户设定比例尺的长度、位置和颜色等参数，以适应不同的绘图需求。使用时需确保轴的DataAspectRatio属性设置为[1 1 1]，并保证视图为二维。SCALEBAR函数支持多种可选参数，如ScaleLength用于设定比例尺长度，位置参数包括东北、西北、东南和西南等选项，还可以自定义比例尺的颜色及文字样式。

基于 MATLAB 的图像 二维小波变换，以及图像 重建。通过小波变换，可以有效地对图像进行压缩和去噪，从而提高图像质量。将介绍如何使用 MATLAB 实现这一过程，包含相关代码示例和关键步骤的详细说明。

这对于处理时需要将3D图像转换为2D图像进行配准的情况非常有用，例如基于地标的薄板样条方法。

《RRT_Star算法在三维与二维路径规划中的应用》RRT（Rapidly-exploring Random Trees）算法是一种用于复杂环境中寻找机器人路径的有效方法，属于概率道路规划的一种。其核心思想是通过随机生成树节点并逐步扩展树来探索配置空间，找到从起点到目标点的可行路径。在此基础上，RRT*（RRT Star）进一步优化，确保路径逐渐收敛到最优解。 本压缩包“RRT_Star_Algorithm.zip”包含RRT算法在三维和二维环境下的实现，提供了在MATLAB平台上的源代码，用户可根据需求进行修改。MATLAB因其强大的可视化功能*，非常适合进行路径规划仿真。 2D环境中的RRT*算法 二维环境中的RRT算法处理平面上的路径规划问题，例如无人机在二维空间中的飞行路径。算法通过在起点周围随机生成节点，选择离树最近的节点进行扩展，直线连接新节点并迭代直至找到目标点。2D文件夹*下代码展示了如何构建和优化搜索树。 3D环境中的RRT*算法 三维路径规划则适用于机器人在立体空间中的移动路径，如仓库机器人。三维空间中，路径不仅考虑x、y方向，还需处理z轴高度变化。3D文件夹中的代码展示了如何扩展RRT*算法处理三维空间路径规划，包括如何生成随机点、选择最近邻节点及更新树结构以逼近最优解。 RRT算法的优势在于其能有效处理高维配置空间，并在动态环境中适应性强，随着迭代，路径逐渐优化趋近最优解。用户可以通过阅读license.txt*文件了解使用许可协议，并对代码进行调整以适应不同的路径规划需求。

Quaternions are an extension of the complex number concept, playing a crucial role in 3D space rotation. To better understand the relationship between quaternions and 3D rotations, we begin with the fundamentals of complex numbers, as there are notable parallels between the properties of complex numbers and their role in 2D rotation. Complex numbers consist of real and imaginary components, where the imaginary unit (i) is defined as ( \sqrt{-1} ), so any complex number (z) can be represented as ( z = a + bi ), with (a) as the real part and (b) as the imaginary part, where (i^2 = -1). Complex numbers can be viewed as points or vectors in the complex plane, where the real part corresponds to the x-axis and the imaginary part to the y-axis. Complex addition and subtraction are straightforward, performed by adding or subtracting the real and imaginary parts, respectively. Multiplication of complex numbers, however, is slightly more complex, requiring expansion and simplification using (i^2 = -1). A significant feature of complex multiplication is its connection to matrix multiplication. Specifically, every complex number can be associated with a particular 2x2 real matrix. For example, the complex number ( z = a + bi ) can be represented by the matrix: [ \begin{bmatrix} a & -b \\ b & a \end{bmatrix} ] This matrix form allows us to use matrix multiplication to perform complex number multiplication. During this process, we observe that complex number multiplication effectively corresponds to a rotation and scaling transformation in 2D space. For instance, the unit complex number 1 corresponds to the identity matrix, leaving points in the complex plane unchanged, while the imaginary unit (i) corresponds to a 90-degree rotation matrix, meaning that multiplying a complex number by (i) rotates it 90 degrees clockwise. After grasping these complex fundamentals, we move to quaternions. Quaternions are a further extension of complex numbers into four-dimensional space. Each quaternion consists of a real part and three imaginary parts, generally written as ( q = w + xi + yj + zk ), where (w) is the real part and (x), (y), and (z) are the imaginary parts. The units (i), (j), and (k) follow distinct multiplication rules in four-dimensional space, where quaternion multiplication is analogous to complex multiplication yet complies with four-dimensional rules. Quaternions are highly beneficial in 3D rotation operations because they avoid gimbal lock, a common issue with Euler angle representation. This makes quaternions a stable and continuous method for rotation interpolation, essential in computer graphics for 3D animation and simulation. Through quaternions, we can use linear algebra and basic geometry principles to intuitively derive and comprehend 3D spatial rotation. Quaternions provide direct representation of the rotation axis and rotation angle, while interpolating quaternions enables smooth transitions between two rotations. This is extremely helpful in animation and real-time rendering for creating fluid movement. For example, in game development and flight simulation, quaternions are often used to achieve coherent flight paths, allowing engineers to implement smooth motion.

彩色点图用于随机数据点可视化，模仿连续的2D概率分布。函数Coldotplot(x,y,s0,Ad)在散点图上创建大小与它们在点群中的密度相对应的点。较大的点在密集的区域也将具有更“热”的颜色。数据x和y是相同大小的向量，s0是每个数据点周围的局部半径参数（默认值= 0.5）。Ad是加权点区域的可视化参数（默认值= 1）。警告：对于非常大的x和y尺寸可能会很慢。这个基础版本可以进一步改进。%例子： N = 1000； x=randn(1,N); y=10*randn(1,N); s0 = 0.5; Ad=0.2； %绘制密度颜色图Coldotplot(x,y,s0,Ad)