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idealfilter
在MATLAB环境下，使用FFT实现的理想低通滤波器。(FFT implementation of the ideal low-pass filter。)
- 2009-03-23 20:01:10下载
- 积分：1
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ncut_multiscale
多尺度normalized cut分割的matlab代码
(Multiscale Normalized Cuts Segmentation Toolbox)
- 2010-01-15 12:55:52下载
- 积分：1
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c6
说明： 若锗中含有一定数量的杂质元素Sb，试根据要求分析杂质浓度与电离度以及电离温度之间的关系：
（1）当Sb浓度分别为 和 时，计算杂质99％，90％和50％电离时的温度各为多少？
（2）根据一定杂质类型和杂质浓度，画出电离度和温度的关系图线，并确定半导体处于强电离区（电离度>90％）的温度范围。
设计要求：（1）具有友好输入输出界面；
（2）调整输入数据，得出相应结果，并进行分析。
(If the germanium containing a certain number of impurity elements Sb, test analysis in accordance with the requirements of impurity concentration and ionization degree, as well as the relationship between the ionization temperature: (1) When the Sb concentration and time were calculated 99 percent of impurities, 90 and 50 ionization When the temperature of each number? (2) according to certain types of impurities and impurity concentration, ionization degree and temperature to draw the relationship between the graph lines, and to determine the semiconductor in a strong ionization zone (ionization degree)
- 2008-07-16 19:58:46下载
- 积分：1
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brindha.ref
need for source code using matlab
- 2013-11-07 14:22:09下载
- 积分：1
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Futures-forecast-PSO-SVM-master
说明： 运用粒子群算法优化支持向量机的回归型预测实例(An example of regression prediction)
- 2021-03-24 12:13:22下载
- 积分：1
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mgelman.m
Elman neural network prediction Mackey-Glass serie
- 2012-05-16 22:17:12下载
- 积分：1
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sparse_in_time
demonstrates the compressive sensing using a sparse signal in Time domain. The signal consists of a UWB (Ultra Wide Band) pulse in time domain. The signal is sparse in Time domain and therefore K random measurements are taken in Frequency domain.
- 2013-03-23 09:10:04下载
- 积分：1
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CardinalPointsFinder
说明： matlab光线追迹例子，用于三片式光学结构追迹仿真(matlab for optic ray tracing)
- 2021-02-06 19:09:56下载
- 积分：1
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MATLABjichu
说明： 这是一本有关matlab的的基础书籍，对初学matlab的人非常好，简单易懂，希望能对你有用(This is a basis for the matlab' s books for beginners matlab are very good, easy to understand, I hope can be useful to you)
- 2010-04-24 18:20:15下载
- 积分：1
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Process
Signal subspace identification is a crucial first step in many hyperspectral processing algorithms such as target detection, change detection, classification, and unmixing. The identification of this subspace enables a correct dimensionality reduction, yielding gains in algorithm performance and complexity and in data storage. This paper introduces a new minimum mean square error-based approach to infer the signal subspace in hyperspectral imagery. The method, which is termed hyperspectral signal identification by minimum error, is eigen decomposition based, unsupervised, and fully automatic (i.e., it does not depend on any tuning parameters). It first estimates the signal and noise correlation matrices and then selects the subset of eigenvalues that best represents the signal subspace in the least squared error sense. State-of-the-art performance of the proposed method is illustrated by using simulated and real hyperspectral images.
- 2013-01-01 20:25:49下载
- 积分：1
