基于推测度传播的同步CDMA多用户检测器，程宏，谢俗子，本文提出了基于可信度传播算法应用于同步CDMA系统的检测器以抑制不同用户之间的多址干扰（MAI）。仿真分析表明基于该算法的差错概��

2D-CNN向前传播 在Vitis HLS中使用C ++实现2D卷积神经网络 CNN-使用-HLS 目录结构： 该软件包包含以下目录： modules /-用于开发和测试用于CNN实施的单个HLS功能的目录 neuronetwork_stream /-包含C ++源代码和测试平台的目录 py /-包含用于训练神经网络的python代码的目录 使用的软件： 操作系统：Windows Vivado HLS 2020.2-仿真结果与综合 Python库 numpy-版本1.18.0 tensorflow-版本2.1.0 sklearn-版本0.24.1 scipy-版本1.6.2

必备资料 Sky-wave propagation and circuit performance at frequency below about 30khz

无线通信类不错的学习材料 This book has grown out of my teaching and research at the University of Surrey and out of my previous experiences in companies such as Philips, Ascom and Motorola. It is primarily intended for use by students in master’s level and enhanced final-year undergraduate courses who are specialising in communication systems and wish to understand the principles and current practices of the wireless communication channel, including both antenna and propagation aspects. I have therefore included examples and problems in each chapter to reinforce the material described and to show how they are applied in specific situations. Additionally, much of the material has been used as parts of short courses run for many of the leading industrial companies in the field, so I hope that it may also be of interest to those who have a professional interest in the subject. Although there are several excellent books which cover portions of this material and which go deeper in some areas, my main motivation has been to create a book which covers the range of disciplines, from electromagnetics to statistics, which are necessary in order to understand the implications of the wireless channel on system performance. I have also attempted to bring together reference material which is useful in this field into a single, accessible volume, including a few previously unpublished research results.

zip 文件包括以下程序： BPM_free_space.m - 高斯脉冲在自由空间中的传播 BPM_triangle.m - 三角形折射率剖面波导中高斯脉冲的传播 BPM_2step.m - 平行矩形波导中渐逝波现象的演示 BPM_Y_Branch.m - Y 分支耦合器中的 FFT-BPM BPM_mach_zender.m - mach-zender 光开关的 FFT-BPM 模拟 参考： K. Okamoto, Fundamentals of Optical Waveguides (Academic, 2000).ISBN-13: 978-0125250955

用反向传播方法解决全波非线性逆散射问题 该Matlab代码用于通过BPS用卷积神经网络解决逆散射问题。 版权所有:copyright:2019，新加坡国立大学，准威。 请先下载matconvnet 1.0-beta23： ://www.vlfeat.org/matconvnet/记住将其压缩。 要使用mex，您还需要安装Visual Studio。 （1）Matlab代码用于在Z.Wei和X.Chen，“全波非线性逆散射问题的深度学习方案”中实施反向传播方案（BPS），IEEE地理科学与遥感学报，57（ 4），第1849-1860页，2019年。此Matlab代码用于通过BPS解决卷积神经网络的逆散射问题，该方法由准威（weizhun1010 @ gmail。com）编写。 如有任何疑问，请随时联系。 仅需要CPU，您可以轻松地将其调整为GPU版本或Python版本。 （2）训练后，您可以通过运行“

Wiley)The Mobile Radio Propagation Channel(2nd Edition).pdf

Factor graphs, belief propagation and variational__inference.pdf

Affinity Propagation (AP) 聚类是最近在Science杂志上提出的一种新的聚类算法。它根据N个数据点之间的 进行聚类,这些相似度可以是对称的,即两个数据点互相之间的相似度一样(如欧氏距离);也可以是不对称的,即两个数据点互相之间的相似度不等。这些相似度组成N×N的相似度矩阵S(其中N为有N个数据点)。AP算法不需要事先指定聚类数目,相反它将所有的数据点都作为潜在的聚类中心,称之为exemplar。

算法可以对各种数据进行聚类分析，具体的应用领域包括图像、文本、生物信息学等等