Staggered-grid-acoustic-simulation

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Upsample Resample Interpolation

matlab code for Firefly Algorithm

这是一个用小波对含噪信号进行滤波处理的程序，程序中还包括了功率谱密度分析等。(This is a wavelet of noisy signal filtering procedures, procedures also include the power spectral density analysis.)

MATLAB CODE FOR INFORMATION THEORY AND CODING

matlab 程序 进行优化分析 使用的方法是惩罚函数法进行优化。(it is called penlty methods，it can optimize the function ,obtaining the minium value)

灰色预测模型，可用于预测未来能源消费的需求量。(Gray prediction model can be used to predict future demand for energy consumption.)

实现典型相关分析，计算加权加速度,基于chebyshev的水声信号分析，用于建立主成分分析模型，线性调频脉冲压缩的Matlab程序,相控阵天线的方向图（切比雪夫加权），鲁棒性好，性能优越。 ( Achieve canonical correlation analysis, Weighted acceleration, Based chebyshev underwater acoustic signal analysis, Principal component analysis model for establishing, LFM pulse compression of the Matlab program, Phased array antenna pattern (Chebyshev weights), Robustness, superior performance. )

本论文对道路提取算法进行分析，综合，同时对特征进行了细致的分析综合，提出了相应的算法(road detection)

这里介绍的案例研究演示如何使用MATLAB来建立测量的燃气经营权，在新英格兰的植物组合相关的风险的申请。在Excel中的应用与实施用MATLAB进行了分析所有的接口。该应用程序允许用户指定的7种植物，包括能力，热率，可变操作和维护成本，最低运行时的特点。这个组合可以用一个简单的调度backtested历史天然气和电力价格的策略来计算利润和机组运行的历史数据。风险措施，通过模拟计算到使用天然气和混合模型在MATLAB中实现，模拟市场价格为每调度方案和计算现金流量从厂房的运作产生未来的电力价格。该现金流量的分布进行分析，产生了90％和95％的现金流量在风险衡量每个工厂以及用于发电资产组合。这些功能全部是提出了一个简洁的Excel的前端。 题为“介绍ETRM为例”的文件将指导，通过分析不同的组件。(The case study presented here demonstrates using MATLAB to build an application for measuring the risks associated with a portfolio of gas-fired power plants operated in New England. The application has an interface implemented in Excel with all of the analytics performed by MATLAB. The application allows the user to specify the characteristics of the 7 plants including the capacity, heat rate, variable operation and maintenance costs and minimum run time. This portfolio can be backtested using a simple dispatch strategy on historical gas and electricity prices to compute historical profit and plant operation statistics. The risk measures are computed by simulating gas and electricity prices into the future using a hybrid model implemented in MATLAB, simulating the dispatch for each scenario of market prices and computing cash-flows arising from the operation of the plants. The distribution of the cash-flows is analyzed to produce a 90 and 95 cash-flow at risk measure for each plant )