three_machines_nine_buses0

SlidingMenu Library for Andorid

TOA定位算法，代码无错误，根据站点信息，闪电位置计算出接受信息，然后加误差反演出数据(TOA for matlab)

This is the previous year question paper of Software Engineering and Methodologies of M.Tech CSE

图像dct变换，量化，f3隐写，压缩，解压，使用matlab实现(Image DCT transform, quantization, F3 steganography, compression, decompression, using MATLAB to achieve)

数学方法是部分子空间法，是机器学习的例程，时间序列数据分析中的梅林变换工具，验证可用，到达过程是的泊松过程，信号维数的估计，music高阶谱分析算法，旋转机械二维全息谱计算。( Mathematics is part of the subspace, Machine learning routines, Time series data analysis Mellin transform tool, Verification is available, Arrival process is a Poisson process, Signal dimension estimates, music higher order spectral analysis algorithm, Rotating machinery 2-d holographic spectrum calculation.)

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.)

在平滑信号中加入高斯分布的白噪声，然后用db4小波函数进行多尺度分解，滤除白噪声，将滤波前后的图进行对比(Add the Gaussian distribution of the white noise in the smoothed signal, and then multi-scale decomposition db4 wavelet function, the filtered white noise, comparing before and after filtering)

BIT PATTERN ANALYSIS OF COMMUNICATION SIGNALS

IIR滤波器的matlab实现，代码精简，实用。(IIR filter matlab realize, code streamlining and practical.)

用matlab中的SVD函数实现线阵或圆阵的music算法的仿真(use svd function to perform music )