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在FPGA开发中，使用ZYNQMPSOC从QSPI闪存启动并挂载JFFS2根文件系统是一项常见的任务。本文将详细介绍如何实现这一过程，并提供相应的源代码。
硬件准备
在开始之前，需要准备以下硬件：
软件准备
在开始之前，需要准备以下软件：
创建Vivado工程
使用Vivado创建一个新的工程，并进行以下设置：
设计FPGA逻辑
在Vivado中设计FPGA逻辑，根据项目需求添加适当的IP核和逻辑电路。确保逻辑电路正确连接到ZYNQMPSOC器件。
生成比特流文件
在Vivado中生成比特流文件（bitstream），将其下载到ZYNQMPSOC开发板上。
创建SDK工程
打开Xilinx SDK，创建一个新的工程，并进行以下设置：
ZYNQMPSOC从QSPI启动并挂载JFFS2根文件系统
jffs2 petalinux qspi
Xilinx ZYNQ 7000学习笔记三(qspi flash读写操作)
ZYNQ 7000系列flash控制器读写
Zynq MPSoC：支持本土存储解决方案 QSPI FLASH 启动 FPGA 开发
使用 SDK 提供的函数和接口，我们可以编写应用程序代码，以实现对 QSPI FLASH 存储器的读写操作。然后，我们擦除 QSPI FLASH 存储器中的数据，写入一段数据，并从中读取数据。确保将 QSPI FLASH IP 核添加到设计中，以支持对 QSPI FLASH 存储器的访问。通过以上步骤，我们可以成功地使用 Zynq MPSoC 板上的 QSPI FLASH 存储器作为启动介质，支持 FPGA 开发。在项目设置中，选择正确的目标设备，确保其与使用的 Zynq MPSoC 开发板相匹配。
ZYNQ7045petalinux挂载jffs2文件系统.pdf
XILINX zynq系统的petalinux工程挂载jffs2文件系统的petalinux配置工作流程，已成功应用下载到目标板卡，板卡重新启动所建立的文件夹和文件不会丢失。
Truncated SVD for faster dection
Truncated SVD方法最先被提出在《Fast R-CNN》论文中，用于降低全连接层的运算量，提升模型的速度。在卷积神经网络的分类任务中，FC层的运算速度远远大于CONV层，而在Fast R-CNN的目标检测任务中，大多数的运算时间发生在FC 的运算中，是因为针对每张输入图片，只进行一次卷积操作，而要对生成的Region Proposal，大致两千个，进行全连接操作进行分类与Bounding...
RcisTarget实战：从差异基因到转录因子调控网络的完整分析流程（附代码）
本文详细介绍了RcisTarget工具在转录因子调控网络分析中的完整流程，从差异基因到motif富集分析，再到结果可视化与生物学解读。通过实战代码演示，帮助研究者掌握这一强大的R包应用，揭示基因表达背后的调控机制，特别适用于基因组学和生物信息学研究。
基于STM32F103C8T6的红外循迹避障小车
编程思路：控制核心板IO口的电平（即电机驱动模块的AIN1，AIN2，BIN1，BIN2）从而控制AO1、AO2、BO1、BO2的电平高低，进而控制电机使其正转、反转、不转。由于单纯地拉高拉低电平只能让电机在转与不转的状态切换，故决定利用TIM4的两个通道分别产生两路PWM信号来控制电机速度，四个电机两个一组，同一侧的两个连接在同一个PWM上。(#include "dianji.h"文件中同时写了对循迹模块引脚的初始化)
伺服驱动器485控制（1）：使用串口软件来读参数
下面介绍如何读伺服驱动器的一圈脉冲数，通过查看伺服驱动器说明书，得知一圈脉冲数的参数是PA11 其通讯地址是十进制 11 十六进制 0A， 串口软件指令都是以十六进制表示，字节单位。注意：485通讯地一定要接，如果不接可能会通讯异常，假如上位机没有485通讯地，而接了两根485+ 和485-，出现通讯不好和反应慢的情况，请把485GND接在上位机0V。笔记本的网口不要和驱动器了网口用网线连接，笔记本网口是以太网，伺服驱动器网口是485。通过驱动器按键面板查看PA11的参数是10000，因此数据对的上。
别再只用线性回归了！用sklearn的核岭回归搞定非线性数据（附RBF核调参实战）
本文详细介绍了如何使用sklearn的核岭回归处理非线性数据，特别是RBF核的应用与调参实战。通过网格搜索找到最佳参数组合，核岭回归能有效捕捉数据中的复杂波动，显著提升模型性能。适合需要解决非线性问题的数据科学家和机器学习工程师。
Python分布式爬虫深度实战：反爬破解+数据清洗+性能优化全流程
分布式爬虫的核心是“调度中心化+执行分布式”，Scrapy-Redis是最易落地的框架，无需从零写调度逻辑；反爬破解要分级应对：基础反爬靠随机UA/限流，进阶反爬靠代理池，高级反爬靠第三方验证码API；数据清洗的关键是“批量处理+标准化”，避免内存溢出，核心字段去重和异常值过滤是必做步骤；性能优化不是单纯加节点，而是从架构（分片）、代码（批量写入）、服务器（索引/网络）三层协同优化，才能实现效率翻倍。
宇树G1开发实践--PPT汇报（遥控版）
本文介绍了一种基于遥控器交互的机器人演示控制系统。系统通过DDS中间件接收遥控器信号，采用位掩码边沿检测技术实现按键精准响应，将不同按键映射为播放控制命令（暂停/重播、跳转、跳过等）。核心交互逻辑包含主播放循环、命令处理和暂停机制，支持播放过程中的实时控制。与自动顺序播放版相比，该系统显著提升了演示灵活性，适用于需要人机互动的演讲场景。使用前需确保网络连接和设备安全，并熟悉按键功能映射。该系统实现了专业级的人机协同演示体验。
在 TASKPROCESSING 上为 ABAP OData 服务添加多个系统别名：从原理到落地的完整实践
本文面向SAP Gateway与ABAP开发者，详细讲解如何在TASKPROCESSING OData服务上添加系统别名。首先介绍了系统别名的概念与设计初衷，阐述了添加别名的业务场景需求，包括多系统接入、用户分流和灰度发布等。随后提供了标准配置步骤，从SPRO路径到具体操作说明，并分享了三个实际案例说明如何实现跨区域统一待办、按用户角色分流和灰度发布。最后给出了验证方法和常见问题解决方案，帮助开发者掌握这项基础但关键的配置技能，实现服务路由的可控管理。
Probability and Statistics - 4th edition
Probability and Statistics - 4th edition
ssm框架搭建
这个是一个在idea上成功跑起来的ssm框架项目，保存在这里方便以后使用直接下载
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")
Devexpress16.2.6破解工具
Devexpress16.2.6破解工具Devexpress16.2.6破解工具Devexpress16.2.6破解工具Devexpress16.2.6破解工具
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