xingtailvbo

利用单带滤波器来设计一个半带滤波器，并显示半带滤波器H0,H1,及 H0+H1的幅频响应单带滤波器的归一化截止频率为0.84,长度＝16，半带滤波器的长度＝31。(Single-band filter design using a half-band filter, and display the half-band filter H0, H1, and H0 H1 magnitude response of the single-band filter normalized cutoff frequency of 0.84, the length of = 16, half-band filter length = 31.)

kalman filter estimator

说明：  这是matlab仿真程序。请喜欢的人下载参考。(this is matlab program, please evrybody like download.)

一个改进的自适应的遗传算法,经测试运行正常.(An improved adaptive genetic algorithm, have been tested and working correctly.)

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.)

关于脑电信号中机械噪声，工频噪声，以及基线漂移的处理，以及提取脑电信号中的特质点，应用matlab中的小波变换来进行消噪找特征点(On the EEG machine noise, frequency noise, and baseline drift of the processing, as well as the characteristics of EEG in point, application of wavelet transform in matlab to find feature points for Denoising)

Flow using diffuse connectivity Research code. Not for commercial use. If you use this code for results in your papers, please cite: A roadmap to the integration of early visual modules, A. S. Ogale and Y. Aloimonos, International Journal of Computer Vision: Special Issue on Early Cognitive Vision Usage: [bestshiftsX, bestshiftsY, occlL, bestshiftsRX, bestshiftsRY, occlR] = flow(iLt, iRt, shiftrangeX, shiftrangeY, alpha) Created by: Abhijit Ogale (ogale@cs.umd.edu)

This .cpp gives a basic foundation of how to manage a big gym. It offers some functions like accounting, expertise giving etc.

compass 03 digital compass module simulation

microstrip microwave filter source code