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原文地址：http://www.cnblogs.com/k1two2/p/5705149.html
https://github.com/demantz/RFAnalyzer
This is the repository of the RF Analyzer app for Android. It can be used to view a FFT plot and a waterfall plot of the frequency spectrum received by a HackRF. The new version can also demodulate audio from AM and FM signals!
See http://tech.mantz-it.com and @dennismantz for updates.
RF Analyzer is also available on Google Play: https://play.google.com/store/apps/details?id=com.mantz_it.rfanalyzer
软件定义无线电SDR入门：原理、硬件选型与实战项目详解
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Android代码-RFAnalyzer
RF Analyzer for Android This is the repository of the RF Analyzer app for Android. It can be used to view a FFT plot and a waterfall plot of the frequency spectrum received by a HackRF. The new version can also demodulate audio from AM and FM signals! (photo by Dennis Mantz) See http://tech.mantz-it.com and [@dennismantz] (https://twitter.com/dennismantz) for updates. RF Analyzer is also available on Google Play: [https://play.google.com/store/apps/details?id=com.mantz_it.rfanalyzer] (https://
【亲测免费】 探索无线电频率的利器：Android版RF Analyzer
RF Analyzer是一款专为Android设备设计的高级频谱分析应用，它能帮助你查看快速傅里叶变换（FFT）图和瀑布图，以实时监控由HackRF或RTL-SDR接收器捕获的频率谱。不仅如此，这款应用还支持AM和FM信号的音频解调，让你的智能手机瞬间变成一款便携式无线电频谱侦探工具。 ## 2、项目技术分析 RF Analyzer利用了`hackrf_android`库与Martin Mar...
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如何用Android手机变身无线电侦探？RF Analyzer让频谱分析触手可及！
想随时随地探索无线电频谱的奥秘吗？RF Analyzer是一款专为Android设备设计的强大频谱分析应用，它能将你的智能手机或平板变成专业的无线电信号检测工具，支持HackRF和RTL-SDR等硬件接收器，让你轻松查看实时FFT频谱图和瀑布图，甚至解调AM/FM信号。无论你是无线电爱好者、电子学习者，还是需要快速检测信号的工程师，这款开源工具都能满足你的需求。 ## ???? 什么是RF Anal...
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【亲测免费】 探索无线世界：Android版RF Analyzer
RF Analyzer是一款专为Android设备设计的频谱分析应用，它允许您查看FFT（快速傅里叶变换）图和瀑布图，以捕捉HackRF接收的频率范围。新版还添加了AM和FM信号的音频解调功能，让您的Android设备瞬间变成一个移动的无线电实验室。 个顶点.设x1,x2,...,xn+1为多面体的顶点,且满足:f(x1)<=f(x2)<=...<=f(xn+1)Nelder-Mead法试着将多面体中最差的顶点xn+1(也就是函数的最大点)以新的最佳点替代,...
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你是否曾想过将Android手机变成专业的软件定义无线电（SDR）接收站？随着无线通信技术的快速发展，移动设备上的SDR应用需求日益增长。rtl_tcp_andro-正是这样一个创新的开源项目，它将RTL-TCP协议扩展到Android平台，不仅兼容所有标准RTL-TCP客户端，还支持控制HackRF等多种SDR硬件设备，为移动端无线频谱探索提供了强大支持。 ## 核心价值：打破硬件壁垒的统一S
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深度学习工具包 DeepLearningToolbox
深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/)I would suggest you use one of the tools mentioned above rather than use this toolbox.Best, Rasmus.DeepLearnToolbox================A Matlab toolbox for Deep Learning.Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.It is inspired by the human brain's apparent deep (layered, hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)```@MASTERSTHESIS\{IMM2012-06284, author = "R. B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[](https://bitdeli.com/free "Bitdeli Badge")
RFAnalyzer 项目教程
RFAnalyzer 是一个用于 Android 的频谱分析器，使用 HackRF 设备接收频率信号。项目的目录结构如下： ``` RFAnalyzer/ ├── app/ ├── doc/ ├── gradle/wrapper/ ├── hackrf_android/ ├── tools/ ├── .gitignore ├── COPYING ├── RFAnalyzer.apk ├── RF...
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该资源为搭建Linux系统Java环境用，资源的后缀为tar.gz格式的压缩包
5大核心功能：RF Analyzer如何将手机变成专业频谱分析仪
想要随时随地分析无线信号吗？RF Analyzer这款安卓应用通过软件定义无线电技术，让你的手机瞬间变身为专业频谱分析仪。无论你是无线电爱好者、工程师还是学生，都能轻松掌握无线信号的频谱特性。 ## 📡 多信号源支持：连接各类SDR设备 RF Analyzer支持多种软件定义无线电设备，包括HackRF、RTL-SDR、Airspy和HydraSDR等。只需通过USB-OTG适配器将SDR设
RFAnalyzer开源项目使用教程
--- ## 项目介绍 RFAnalyzer是一个由[@demantz](https://github.com/demantz)开发的开源工具，专注于无线电频率分析和监测。该项目旨在提供一个简单易用的界面来捕捉并解析无线电信号，适用于无线电爱好者、物联网(IoT)开发者以及进行无线通信研究的人员。它利用了SDR（软件定义无线电）硬件的强大功能，使得非专业用户也能进行频谱分析。 --- ##...
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