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Source
Source AWGN random bit
- 2010-10-18 20:51:27下载
- 积分：1
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hetman
hetman problem and c source code
- 2010-11-29 04:52:56下载
- 积分：1
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MITmatlab
美国麻省理工学院MIT的MATLAB介绍，值得一读!(book)
- 2009-05-14 20:06:17下载
- 积分：1
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matlab
用matlab进行的有关无线传感器网络定位的研究(Carried out using matlab on the positioning of wireless sensor network research)
- 2009-05-14 21:43:24下载
- 积分：1
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PSKRecovery
M-PSK Recovery for Modem Design.
- 2013-08-25 18:45:06下载
- 积分：1
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shooting
shooting 打靶法，MATLAB编程，仅供参考。(shooting shooting method, MATLAB programming, for reference only.)
- 2021-01-29 00:48:39下载
- 积分：1
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fftview
做信号处理的时候经常会用到FFT，而MATLAB自带的FFT只能变换出一列系数，想做成频谱图还要自己加一部分，自己写了一个小程序，输入原始信号和采样率，可以画出标准的幅值谱和功率谱。(Do signal processing is often used when the FFT, and FFT MATLAB own transformation from a coefficient of only wanted to make Canada part of the spectrum but also their own, to write a small program, the input original signal and the sampling rate can be Draw a standard amplitude spectrum and power spectrum.)
- 2011-02-01 12:07:10下载
- 积分：1
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1
说明： 比例导引法的MATLAB，程序，希望大家多侃侃(matlab)
- 2009-08-12 14:24:56下载
- 积分：1
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passage-3
本程序是mazen.O的经典论文：Performance Analysis of two-hop ralayed transmissions over rayleigh fading channels 的文章中所附的仿真图的程序，跑出的结果和文章中的附图是一样的！可以跑出里面的三张图，包括瑞利衰落下中继信道的中断概率的蒙特卡洛及公式的仿真，及选择不同的增益的性能差别曲线！
(This program is mazen.O classic paper: Performance Analysis of two-hop ralayed transmissions over rayleigh fading channels in the article accompanying the simulation of the processes, results and articles ran in the figures are the same! You can run out of the inside of the three figures, including the Monte Carlo simulation and the formula for the outage probability of Rayleigh fading relay channel, and select a different gain performance difference curves!)
- 2020-10-05 15:07:39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
