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BER_APSK
16apsk通讯系统的信噪比与误码率计算并绘图(16APSK communications system signal to noise ratio and bit error rate calculation and mapping)
- 2008-12-11 18:13:27下载
- 积分：1
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S_Function
本课件主要讲述MMatlab Simulink中S函数(Function)的写法,其中包含非常实用的S-函数介绍（为什么要引入S-Function？)、S-函数的分类、使用（实例演习）等(The courseware in the main about MMatlab Simulink S function (Function) is written, which contains the S-function is very useful Introduction (why the introduction of S-Function?), S-function category, the use of (example exercises), etc.)
- 2011-01-29 12:27:05下载
- 积分：1
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Matlab_Dsp_Builder_DEsign_Guide
说明： 使用matlab中的simulink，通过DSP Builder进行编译成quartus可用的vhdl文件，简化实际工作量的流程方法(Use matlab in simulink, through the DSP Builder to compile into a vhdl file quartus available, simplifying the process method of the actual workload)
- 2009-07-30 09:32:01下载
- 积分：1
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moravec
利用moravec算子提取特征点，非常经典，基本上其他的一些特征点提取算子是在它的基础上被开发出来的(Feature extraction using moravec point operator, very classic, basically some other feature extraction operator is based on it being developed by)
- 2010-05-18 16:01:21下载
- 积分：1
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magic-cube-in-matlab
用于初学者在matlab中绘制魔方，初学者可以下载学习其中步骤(magic cube in matlab)
- 2013-05-21 03:08:36下载
- 积分：1
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11
说明： 为了了解地震波的传播，用有限差分对二维声波传播进行模拟(Solving wave wave equation with finite difference)
- 2018-01-11 16:19:46下载
- 积分：1
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5
说明： 液晶空间调制器的非线性及其在闭环系统中的应用(Nonlinear liquid crystal spatial modulator and its application in the closed-loop system)
- 2011-03-12 20:23:53下载
- 积分：1
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MATLAB
Matlab 的基础教程 PDF格式 内含命令清单(Essentials of Matlab in PDF format)
- 2012-04-03 01:29:20下载
- 积分：1
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gui_tutorial_images_3
gui tutorial methods part 3
- 2014-02-23 02:01:10下载
- 积分：1
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1807.01622
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
