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rcos_filter
说明： 用于广播系统发射机的数字中频调制采用64QAM的方式，使用matlab中的altera dsp builder实现。(64QAM modulator, used in broadcast system digital IF modulation.)
- 2010-04-20 23:10:24下载
- 积分：1
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MITPMATLAB
麻省理工学院的matlab教程，应该很给力吧，还比较详细(MIT matlab tutorial, it should be to force it, but also more detailed)
- 2011-06-09 22:29:04下载
- 积分：1
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MUSIC-TRANSFORM(Two-Tigers)
产生两只老虎乐曲，并进行了谐波叠加，模拟声音(To produce two tigers music, and harmonic superposition, analog sound)
- 2013-01-13 00:15:02下载
- 积分：1
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Kalman_filtering_algorithm
卡尔曼滤波，详细介绍了其具体过程，写论文必用,一定要收藏(It s very useful to write papers with Kalman filtering algorithms.)
- 2010-07-23 19:01:45下载
- 积分：1
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FDTD_TM_PC
利用时域有限差分法模拟二维光子晶体光纤中光的传播(The use of FDTD simulation of two-dimensional photonic crystal fiber-optic transmission of light)
- 2009-05-14 13:43:11下载
- 积分：1
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Aperture_Effeciency_GUI_Even_only
This MATLAB code plot the aperture efficiency Vs the subtended angle. The user enter the value of n (2,4,6 or 8) and the code will plot and tell what is angle value that achieves max efficiency and the value of this efficiency.
- 2011-05-26 16:17:56下载
- 积分：1
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gps_position
通过Matlab仿真实现GPS定位算法的实现(GPS positioning algorithm and implementation,)
- 2011-09-16 12:56:41下载
- 积分：1
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mser
实现道路检测，能从图像中自动检测并分割出道路，用于交通检测(Achieve road detection, the image automatically detect and segment the road for traffic detection)
- 2014-12-16 13:49:41下载
- 积分：1
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1807.01622
说明： 深度神经网络在函数近似中表现优越，然而需要从头开始训练。另一方面，贝叶斯方法，像高斯过程（GPs），可以利用利用先验知识在测试阶段进行快速推理。然而，高斯过程的计算量很大，也很难设计出合适的先验。本篇论文中我们提出了一种神经模型，条件神经过程（CNPs），可以结合这两者的优点。CNPs受灵活的随机过程的启发，比如GPs，但是结构是神经网络，并且通过梯度下降训练。CNPs通过很少的数据训练后就可以进行准确的预测，然后扩展到复杂函数和大数据集。我们证明了这个方法在一些典型的机器学习任务上面的的表现和功能，比如回归，分类和图像补全(Deep neural networks perform well in function approximation, but they need to be trained from scratch. On the other hand, Bayesian methods, such as Gauss Process (GPs), can make use of prior knowledge to conduct rapid reasoning in the testing stage. However, the calculation of Gauss process is very heavy, and it is difficult to design a suitable priori. In this paper, we propose a neural model, conditional neural processes (CNPs), which can combine the advantages of both. CNPs are inspired by flexible stochastic processes, such as GPs, but are structured as neural networks and trained by gradient descent. CNPs can predict accurately with very little data training, and then extend to complex functions and large data sets. We demonstrate the performance and functions of this method on some typical machine learning tasks, such as regression, classification and image completion.)
- 2020-06-23 22:20:02下载
- 积分：1
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chaos
混沌演示——数据加密的动态过程以及结果展示(Chaos demo- data encryption as well as the results demonstrate the dynamic process)
- 2007-11-12 10:59:15下载
- 积分：1
