Stations Precipitation Analysis Across Different Locations in MATLAB

% Concatene 单元矩阵（按行或按列），无论大小如何，用 NaN 填充缺失的单元。% 该函数概括了之前的函数 K_cRows 和 K_cCols。% % c = K_cCells(b,a,'row') 或 c = K_cCells(b,a,'everyCharOrNumber') 用于按 行； % c = K_cCells(b,a,'col') 用于按 列； % A = {'a11','a12';'a21','a22'} % >> A = 'a11' 'a12' 'a21' 'a22' % >> B = {'b11','b12','b13'; 'b21','b22','b32'; 'b31','b32','b33'} % >> B = 'b11' 'b12' 'b13' 'b21' 'b22' 'b31' 'b32' 'b33' % >> C = K_cCells(A,B) % C

此函数会将任意数量轴的caxis更改为相同。这是通过简单地将每个轴手柄的caxis更改为从所有caxis的最小值到所有caxis的最大值来实现的。

Independent Component Analysis (ICA) stands as a pivotal advancement across diverse fields such as neural networks, advanced statistics, and signal processing. This resource furnishes a thorough introduction to ICA, encompassing the foundational mathematical principles, critical solutions, algorithms, and comprehensive exploration of novel applications in domains like image processing, telecommunications, and audio signal processing. The text meticulously dissects ICA into four core segments:* Fundamental Mathematical Concepts: This section lays the groundwork for understanding the mathematical underpinnings of ICA.* The Basic ICA Model and Solution: A detailed examination of the core ICA model and its associated solution strategies.* Extensions of the Basic ICA Model: Exploration of various extensions to the fundamental ICA model, enhancing its adaptability and applicability.* Real-World Applications of ICA Models: Delving into practical implementations of ICA models across diverse disciplines. The authors, renowned for their contributions to ICA development, provide a comprehensive treatise on relevant theories, cutting-edge algorithms, and real-world implementations, making this an indispensable resource for students and practitioners alike.

MATLAB开发 - LeadingEntryVertime的动画 随着时间的推移，这显示了关于前导条目的各种情况。通过动画，用户可以直观地理解LeadingEntryVertime在不同时间点的表现和变化。

掌握DFT函数的用法。 2. 利用 DFT进行信号检测 及 谱分析。 3. 了解 信号截取长度 对 谱分析 的影响。

语谱图实验介绍 左上角的图形框（the spectrum of the number）表示：输入的数字的频谱图； 左下角的图形框（yuputu）表示：连续输入的号码的语谱图； 右上角的图形框显示输入的数字； 右面中间的16个号码代表按键盘； “hangup”：表示“挂断电话”，同时将左上角的图形框（the spectrum of the number）和左下角的图形框（yuputu）清空； “recall”：表示“重播”，同时将上一次输入的一串号码数值显示（yuputu）中，并将上次号码的最后一个号码的频谱图显示在（the spectrum of the number）中。

雷达系统分析与设计，这本书是雷达的基本入门书籍，内含MATLAB源代码，使用起来比较方便。虽然有中文翻译，但存在很多错误，建议查看英文版！

3.2 Genetic Operators (1) Crossover Operator The crossover operator randomly pairs individuals from the parent population for crossover operations, generating ( m ) offspring individuals to form the next generation. Two types of crossover are employed: single-point crossover and two-point crossover. Given two individuals for crossover ( P = {p_1, p_2, p_3, \dots, p_n} ) and ( Q = {q_1, q_2, q_3, \dots, q_n} ), a random crossover point ( b_1 ) is chosen from the range [1, n] for single-point crossover. The elements before ( b_1 ) in ( P ) are copied to offspring individual ( \text{new Individual1} ), while the remaining elements are copied from ( Q ). Similarly, a second offspring ( \text{new Individual2} ) is generated by swapping the roles of ( P ) and ( Q ). In two-point crossover, two random crossover points ( b_1 ) and ( b_2 ) are chosen, and the elements between ( b_1 ) and ( b_2 ) in ( P ) are copied to the offspring, with the remaining elements taken from ( Q ). (2) Mutation Operator After the crossover operation, two mutation operators are applied to the offspring individuals. The first is rotation mutation, where a random position ( \text{bit} ) is chosen, and with probability ( p_m1 ), the portion of the individual after ( \text{bit} ) is rotated. The second is position mutation, with a smaller probability ( p_m2 ), two integers ( \text{bit1} ) and ( \text{bit2} ) are randomly chosen from the range [1, n], and the corresponding parts of the individual are swapped. (3) Selection Operator The fitness of the mutated offspring individuals is evaluated using the lowest level line method. The parent and offspring individuals are ranked by their fitness in descending order, and the top ( m ) individuals are selected as the next generation's parents. 3.3 Termination Criteria The steps in sections 3.2(1), 3.2(2), and 3.2(3) are repeated until the fitness of the best solution meets the required threshold or the pre-defined number of generations is reached. At this point, the optimal solution is output. 4. Case Study To test the performance of the algorithm, two cases from literature [3] are solved. In Case 1, a large rectangle of size ( 15 \times 40 ) is divided into 25 smaller rectangles. Based on the lowest level line method, the corresponding coding sequence is ( \text{Opt} = {1, -9, 11, -15, 17, -24, -25, -10, -14, -22, -23, -2, -3, -5, 18, 7, -8, -12, 19, -20, 21, 6, 13, 4} ). The width is set at 40, and height considerations follow suit for the genetic algorithm implementation.

该存储库包含我们题为“用于从整个幻灯片图像中提取格里森组织并分级前列腺癌的扩张式残留分层分割框架”的论文的实现。拟议框架的框图所提出的框架是使用TensorFlow 2.3.1和Keras API与Python 3.7.8开发的。此外，一些预处理步骤和结果汇编也通过MATLAB R2020a执行。下面显示了安装和运行代码的详细步骤： 安装 要运行代码库，需要以下库。虽然该框架是使用Anaconda开发的，但它应该与其他平台兼容。- TensorFlow 2.3.1- Keras 2.3.1- OpenCV 4.4.0- tqdm- Matplotlib 另外，我们还提供了一个yml文件，其中包含所有这些软件包。 数据集 请下载所需的数据集，并按照以下提到的层次结构来训练和测试建议的框架： ├── trainingDataset │ ├── train_images │ │ └── tr_image_1.png │ │ └── tr_image_2.png