MATLAB Implementation of Multi-Channel Filter Bank

SUI MIMO channel 的 MATLAB 实现，附有说明文档。

This is the code for implementing the RLS adaptive algorithm filter. The RLS (Recursive Least Squares) algorithm is widely used in adaptive filtering applications. Below is the MATLAB implementation of the RLS adaptive filter which helps in understanding the core concepts of adaptive filtering and recursive algorithms. % MATLAB code for RLS adaptive filter N = 1000; % Number of filter coefficients M = 32; % Filter order lambda = 0.99; % Forgetting factor delta = 10; % Initialization constant % Initialize filter coefficients and variables w = zeros(M, 1); % Filter weights P = delta * eye(M); % Inverse correlation matrix % Simulate the input signal and desired output x = randn(N, 1); % Input signal d = filter([1, -0.9], 1, x); ?sired signal % RLS adaptive filtering loop for n = M+1:N x_n = x(n:-1:n-M+1); % Input vector e = d(n) - w' * x_n; % Error signal k = P * x_n / (lambda + x_n' * P * x_n); % Gain vector w = w + k * e; % Update weights P = (P - k * x_n' * P) / lambda; % Update inverse correlation matrix end % Display results figure; plot(d, 'b'); hold on; plot(filter(w, 1, x), 'r'); legend('Desired', 'Filtered Output'); This code illustrates how to apply the RLS adaptive filter to adjust its coefficients to minimize the error between the desired signal and the filter output.

OFDM传输、信道估计、PN序列、RS码实现、比较

Due to the increasing use of power electronic devices in power regulation, the quality of power systems has been deteriorating. As a solution, passive shunt filters are used to suppress harmonics in these systems. This is achieved through a Simulink model design and implementation, where the passive harmonic filter effectively mitigates the effects of harmonics in the power system.

典型相关分析Matlab实现代码：ML-CCA快速多标签规范相关分析的Matlab实现。ML-CCA 是一种高效的多标签跨模态检索方法，提升不同模态数据之间的相关性度量。引文：如果您在项目中使用此代码，请引用我们的论文： @inproceedings{ranjan2015multi,title={Multi-label cross-modal retrieval},author={Ranjan, Viresh and Rasiwasia, Nikhil and Jawahar, CV},booktitle={Proceedings of the IEEE International Conference on Computer Vision},pages={4094--4102},year={2015}}

Assignment for Chapter 3作业内容： Q1. Bank Database Queries 表结构：- branch(branch_name, branch_city, assets)- customer(customer_name, customer_street, customer_city)- loan(loan_number, branch_name, amount)- borrower(customer_name, loan_number)- account(account_number, branch_name, balance)- depositor(customer_name, account_number) 请构建以下SQL查询： a. 查找所有在“Brooklyn”所有分支都有账户的客户。 SELECT customer_name FROM customer WHERE customer_name IN ( SELECT depositor.customer_name FROM depositor, account, branch WHERE depositor.account_number = account.account_number AND account.branch_name = branch.branch_name AND branch.branch_city = 'Brooklyn' ) GROUP BY customer_name HAVING COUNT(DISTINCT branch.branch_name) = (SELECT COUNT(branch_name) FROM branch WHERE branch_city = 'Brooklyn'); b. 查找银行所有贷款金额的总和。 SELECT SUM(amount) AS total_loan_amount FROM loan; c. 查找资产大于至少一个位于“Brooklyn”的分支资产的所有分支名称。 SELECT DISTINCT branch_name FROM branch WHERE assets > ANY ( SELECT assets FROM branch WHERE branch_city = 'Brooklyn' ); Q2. Employee Database Queries 表结构：- employee(employee_name, street, city)- works(employee_name, company_name, salary)- company(company_name, city)- manages(employee_name, manager_name) 请构建以下SQL查询： a. 查找...(继续书写其他查询)

该工具箱帮助初学者学习侧信道分析的思想、方法和技术。我们提供： (1) 图形用户界面——可视化分析过程； (2) 常用函数库——让人们更容易设计算法。

MATLAB开发的扫雷游戏，一个类似于Windows中的扫雷游戏，但在纯MATLAB中实现。

维特比解码 MATLAB 代码的 Materl Viterbi 解码器算法的实现。维特比算法 作为 卷积码 的最大似然（ML）解码技术而闻名。在 (n, k, m) 维特比解码器 中，路径存储单元负责跟踪与由路径度量单元指定的尚存路径相关的信息位。二进制卷积码 由三元组 (n, k, m) 表示，其中每当接收到 k 个输入位时，就会生成 n 个输出位。k 是输入序列的数量（因此，编码器由 k 个移位寄存器组成），m 表示必须存储在编码器中的先前 k 位输入块的数量。维特比解码器通常基于ASIC，因此在路径存储器的大小上具有上限。为节省路径存储器，提出了一种新颖方法，成功开发了许多使用该路径存储器的回溯式维特比解码器。这表明，使用这种高效存储路径的维特比解码器需要较小的芯片面积，并且在不损失解码性能的情况下实现了更快的解码时间。利用这种新颖路径存储器的维特比解码器可节省 20% 的 (n, 1, m) 码存储，节省 20% 的普通 (n, k, m) 码，而不会降低解码性能。新型路径存储器还具有类似的提高的解码性能。