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本文详细记录了在 NVIDIA Jetson AGX Xavier 边缘计算板上安装 Ubuntu 操作系统及 NVIDIA 开发者工具的过程。介绍了通过 JetPack SDK Manager 安装系统、软件套件的方法，涵盖了 CUDA、cuDNN、OpenCV 和 Docker 的配置。
对近几个月在Nvidia Jetson AGX Xavier上的工作做一下记录备忘，Xavier是NVIDIA的边缘计算板子的最新版本也是最高配的序列，NVIDIA Jetson序列板子的具体配置可参见下图:
因机器人上需要部署网络层次深体量大的高精度AI图像识别物体检测模型，一般的边缘计算板子扛不住，于是让公司购买了NVIDIA Jetson AGX Xavier 16G内存配置的版本用于实验模型部署和调用测试(后面会写Xavier上docker镜像的制作和模型部署环境的安装的实验过程)，Xavier跟一台低档PC电脑的配置差不多了，16G内存，另外内置有32G的eMMC存储（相当于硬盘），里面安装了裁剪到最小化的Ubuntun18.04。
开箱后加电启动后进入个文本命令行界面，里面有下一步安装的说明：
这种最小化版本是为了在做产品化部署时使用节省内存和存储空间(是否支持dokcer和GPU的使用待以后实验)，在上面做实验的话干不了什么活根本不够用，所以决定安装带图形化界面的Ubuntu，最简单的办法
NVIDIA JETSON AGX XAVIER 搭建环境并成功运行算法
现在默认已经对Xavier板子完成了刷机（未刷机可以看我前一次的博客），已经进入到Xavier系统中，按照流程进行开发组件系统安装并设置账号密码，且Xavier能够进行联网。（不能联网看我前一次的博客）如果你的Xavier不需要安装太多库文件或函数，只需要配置运行一种算法可以跳过这步。 但是Xavier自带存储内存只有32g，大多数人还未配置好环境内存就已经满了，所以我建议还是装一个固态硬盘进行扩容。 参考博客：https://www.ncnynl.com/archives/201912/3486.html
NVIDIA Jetson AGX Xavier安装OpenPCDet完整踩坑记录
NVIDIA Jetson AGX Xavier安装OpenPCDet的完整过程，出现的问题及解决方案。
nvidia jetson xavier agx安装pytorch
nvidia jetson xavier agx安装pytorch一、环境二、CUDA版本查看三、pytorch安装1、编译安装2、whl文件安装新的改变功能快捷键合理的创建标题，有助于目录的生成如何改变文本的样式插入链接与图片如何插入一段漂亮的代码片生成一个适合你的列表创建一个表格设定内容居中、居左、居右SmartyPants创建一个自定义列表如何创建一个注脚注释也是必不可少的KaTeX数学公式新的甘特图功能，丰富你的文章UML 图表FLowchart流程图导出与导入导出导入 一、环境 硬件：Nvidia
NVIDIA Jetson AGX Xavier学习笔记2——JetPack安装(刷机)
最近研究中需要使用NVIDIA Jetson AGX Xavier人工智能开发组件。由于也是第一次接触相关硬件设备，遇到了很多困难。在这里记录整个Jetson AGX Xavier组件的学习过程(其中很多内容网上有比较详细的教程，我就直接引用参考的相关资源，感谢这些文章作者的付出)，希望能够帮助到有同样需求的人。 ...
NVIDIA Jetson AGX Xavier 安装 JetPack 注意事项
NVIDIA Jetson AGX Xavier 安装 JetPack 注意事项 这是本人与NVIDIA的边缘计算平台，本文主要记录了我第一次接触Linux和为NVIDIA Jetson AGX Xavier安装JetPack时遇到的一些问题与解决方法。所述内容分为Ubuntu的安装，SDK Manager的安装，SDK Manager的使用，共三大块。其中困扰我最久的是SDK Manager的使用，主要有以下几个问题：1.用SDK Manager为Xavier刷Ubuntu系统时（即flash Jets
Nvidia Jetson AGX Xavier 安装SATA接口固态硬盘
Nvidia Jetson Agx Xavier 安装SATA接口固态硬盘 硬件准备 固态硬盘 固态硬盘的接口决定了其接入开发板的方式。本文使用的是SATA接口的固态硬盘，品牌型号：SAMSUNG 860 EVO 固态硬盘接口分类参考：固态硬盘SATA、M.2都是啥意思，买哪种好？ “目前常见的固态硬盘接口有四种，分别为SATA、M.2、mSATA和PCI-E。 SATA和M.2固态硬盘是普通消费者经常接触的，目前处于主流地位。 mSATA固态硬盘在过去几年比较流行，现如今基本已经被M.2取代了。 PC.
Nvidia Jetson AGX Xavier安装ROS、Anaconda、PyTorch及其它依赖库
Nvidia Jetson Xavier部署ROS跟Ubuntu下稍微有些不同，可以通过ROSXavier脚本安装： https://github.com/jetsonhacks/installROSXavier。 git clone https://github.com/jetsonhacks/installROSXavier.git cd installROSXavier ./installROSXavier
从零开始：NVIDIA Jetson AGX Xavier的Ubuntu系统安装与配置指南
本文提供了一份详尽的NVIDIA Jetson AGX Xavier边缘AI开发板的Ubuntu系统安装与配置指南。文章重点介绍了使用官方JetPack SDK进行完整系统安装的流程，涵盖了从硬件准备、SDK Manager工具使用、系统刷写到核心组件（如CUDA、TensorRT）安装的全过程，并提供了安装后的基础配置、效能调优及常见问题解决方案，帮助开发者快速搭建稳定的AI应用开发环境。
【Jetson】NVIDIA Jetson AGX Xavier 安装系统 配置VNC
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X3850x6 6241 fireware固件升级
登录联想https://datacentersupport.lenovo.com/cn/zc网站，选择system x，x3850 x6，type选择6241 选择驱动及应用程序，BIOS/UEFI 选择lnvgy_fw_uefi_a9e156a-4.90_anyos_32-64.uxz 文件，点击下载 选择IMM2, 下载lnvgy_fw_imm2_tcoo58b-5.80_anyos_noarch.uxz ps：3837和3839会跳转到ibm网站下载 笔记本直连.
DEV-C++调试设置
昨天用C/C++去考了CSP，结果用DEV-C++调试总是闪退，按照我的记忆打开了调试参数，迷迷糊糊搞了一通，还是没办法调试，难受了几个小时。所以今天痛下决心，一定要写下DEV-C++设置调试的过程，警示自己！ 解决步骤 1.打开DevC++找到上方工具栏 2.点击选择编译选项，再按下图所示设置即可
Amesim应用篇-电芯等效电路模型标定
为了使计算模型更加准确，在有电芯实验测试数据的情况下，依据现有的实验数据对Amesim中的电池等效电路模型进行标定。标定的目的是为了获得更加符合项目实际情况的电芯等效电路模型，标定完的电芯可以用于搭建PACK模型，也可以用于其他虚拟实验。本文以充电标定为例，进行介绍。放电标定与充电一致。
计算分子相似度（摩根分子指纹计算）
import numpy as np import pandas as pd import seaborn as sns import matplotlib as plt from rdkit.Chem import Draw from rdkit.Chem import AllChem as Chem from rdkit.Chem import PandasTools from rdkit import DataStructs data = pd.read_csv(r'47_covidLead.csv'
数字IC/FPGA 秋招知识点不全面整理
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2024 全国大学生新质生产力大赛 B：金融违规交易的大数据分析 问题四思路代码
问题四：请分析附件中所有的指标数据，选取合适的指标，建立“金融违规 洗钱交易的预测模型”，并选取合适的训练集和测试集，计算预测模型的准确率。 为了提前识别并预测金融违规洗钱的交易事件，请基于你们团队数据分析的结果， 分别给公安部门和银行机构提出你们的建议。
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")
WINCC7.3破解文件
WINCC7.3硬件够破解文件，有需要的下载！WINCC7.3硬件够破解文件，有需要的下载！
文档：亲测可用解决vnc灰屏，黑屏，鼠标大黑叉情况
主要解决vnc-viewer灰屏，黑屏，鼠标大黑叉等现象，详细见文档。
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