single_point-_position

用周期图法求出的功率谱曲线和burg算法求出的AR功率谱曲线(p=50)(Periodogram calculated using the power spectrum curves and burg algorithm to derive the AR power spectral curve (p = 50))

Matlab中的主成分分析函数，对初学者比较适用(Matlab in principal component analysis function, more suitable for beginners)

二维边界元Matlab程序 附初始条件 运行结果 (2D Matlab BEM program)

blast系统的检测方案仿真程序,在窄带Rayleigh快衰落信道下几种检测方法的误码率性能比较(blast system simulation program testing program, in the narrow-band Rayleigh fading fast detection method of several BER performance comparison)

信号与系统期末大作业。关于信号处理、零极点、时序频域分析。包含完整源程序源代码以及详细的实验报告。(Signals and systems ending job. Signal processing, poles and zeros, timing, frequency domain analysis. Contains the complete source code source code as well as a detailed lab report.)

proposed 2X2 MIMO OFDM transceiver with ICI-SC technique has

Question 1.3 Sullivan

Simplex Program made on Matlab

helpful virtual rality document

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.)