墨衍会员
·
AI 创作全网分发
墨衍智能分发
本文由作者通过墨衍一键同步至各平台
1分发平台
100+累计阅读
148平均单平台
已同步平台
优快云
微信公众号
微博
知乎
掘金
百家号
博客园
抖音
小红书
你的文章也可以这样分发
墨衍会员 ¥399起/年，写一次发全网
我也要
DCMTK：DcmItem类的测试程序 编程
DCMTK（Digital Imaging and Communications in Medicine Toolkit）是一个用于处理医学影像数据的开源软件包。其中的DcmItem类是DCMTK库中的一个关键类，用于表示和管理DICOM（数字成像和通信医学）数据集中的数据项。
在本文中，我们将介绍如何编写一个简单的测试程序，以演示如何使用DcmItem类来创建和操作DICOM数据项。
首先，我们需要安装DCMTK库并设置开发环境。假设您已经完成了这些步骤，我们可以开始编写测试程序。
#include <dcmtk/dcmdata/dctk.h>
int main()
{
// 创建一个空的DICOM数3D医学影像开发入门＜一＞：VS2019+DCMTK3.6.8编译及环境配置
3D医学影像开发入门：VS2019+DCMTK3.6.8编译及环境配置
DCMTK：DcmItem类的测试程序
DCMTK：DcmItem类的测试程序DcmItem类的测试程序 DcmItem类的测试程序 #include "dcmtk/config/osconfig.h" #include "dcmtk/ofstd/oftest.h" #include "dcmtk/dcmdata/dcitem.h" #include "dcmtk/dcmdata/dcvrat.h" #include "dcmtk/dcmdata/dcvrus.h" #include "dcmtk/dcmdata/dcdeftag.h" O
DCMTK：DcmItem和DcmSequenceOfItem的路径功能测试程序
DCMTK：DcmItem和DcmSequenceOfItem的路径功能测试程序DcmItem和DcmSequenceOfItem的路径功能测试程序 DcmItem和DcmSequenceOfItem的路径功能测试程序 #include "dcmtk/config/osconfig.h" #include "dcmtk/ofstd/oftest.h" #include "dcmtk/dcmdata/dctk.h" #include "dcmtk/dcmdata/dcpath.h" static OFL
DCMTK：基于父元素处理的测试程序
在DICOM文件中，父元素是一个包含子元素的数据标签，每个子元素都具有一个唯一标识符和一个值。在processFile方法中，遍历了DICOM文件中的所有元素，并对每个元素创建了一个MyProcessor对象并调用了accept方法来处理它。下面是一个基于父元素处理的测试程序，它可以遍历DICOM文件中所有的父元素，并针对每个元素执行特定的任务。总之，基于父元素处理的测试程序是DCMTK中一个非常有用的工具，可以用来测试DICOM文件读取功能的正确性，并通过继承和重写来实现特定的任务。
DCMTK库中的DcmElement :: calcElementLength函数是用来计算DICOM数据元素长度的，因此我们可以编写一个测试程序来验证该函数的
通过编写这样的测试程序，我们可以验证DCMTK库中的DcmElement :: calcElementLength函数的准确性。这对于确保DICOM数据传输和存储的完整性非常重要。然后，在使用findAndGetElement函数找到该元素后，我们使用calcElementLength函数计算了该元素的长度，并将结果输出到控制台。DCMTK库中的DcmElement :: calcElementLength函数是用来计算DICOM数据元素长度的，因此我们可以编写一个测试程序来验证该函数的准确性。
Android音频学习(十四)——加载音频设备
loadHwModule 是 Android 音频系统的核心初始化函数，负责加载音频硬件抽象层（HAL）的模块实现。它建立 AudioPolicyService 与底层硬件的桥梁，将配置文件(audio_policy_config.xml)中的标签<module>定义转化为可操作的硬件对象，加载的是音频硬件模块（HAL实现），每个模块对应一种音频设备类型，以动态库的形式存在，负责与底层音频硬件交互。
【FreeRTOS】FreeRTOS 源码学习笔记 （3）队列 及 xQueueGenericSend分析
引言 本篇又不写调度器了，准备先整理整理“队列“，无他，就觉得这个很有用，自己写应用层代码的时候，队列要用的比较多，之前一直用一个自己写的环形buffer，里面nReadIndex,nWriteIndex，nFreeIndex来管理总觉得不够“官方”，这次看到一个如此成熟的队列，还是忍不住先分析一下。 ...
低成本主从机械臂系统构建：6D位姿数据采集与VLA模型训练实战
本文详细介绍了如何构建低成本主从机械臂系统，实现高精度6D位姿数据采集与VLA模型训练。通过300美元级别的硬件组合，系统达到±1mm位置精度和≤0.5°姿态误差，满足Vision-Language-Action模型的多模态训练需求。文章涵盖硬件选型、数据采集技巧、模型训练实战经验，为机器人研究提供经济高效的解决方案。
OriginPro实战：5步搞定碳材料拉曼光谱D峰G峰分析（附完整数据处理流程）
本文详细介绍了使用OriginPro进行碳材料拉曼光谱D峰G峰分析的完整流程，涵盖数据预处理、多图层可视化、高精度分峰拟合等关键步骤。特别针对材料科学研究中的基线漂移、多峰重叠等问题提供专业解决方案，帮助研究者快速完成从原始数据到科研级图表的全流程分析，提升碳材料表征效率与准确性。
matlab电容式传感器仿真,差动电容式位移传感器的仿真研究
设计了一种差动电容式位移传感器,主要对传感器的数学模型及电容检测电路进行了仿真研究。其中检测电路设计主要包括正弦激励电路、转换桥路、信号放大电路、精密整流电路和滤波电路。最后利用Matlab／SimurFnk软件对所设计的系统进行仿真,并分析了传感器性能。仿真研究表明,传感器及其检测电路具有合理性,测量范围内具有很好的线性度。0|| I≯ lat on ii{差动容尊翩’亘研发注：湖南省教育厅资...
RTCA DO-160G标准详解：航空电子设备的全面环境适应性测试指南
第二章的主要目的是提供标准中使用的术语和定义。了解这些术语对于准确理解标准的要求至关重要，特别是在技术实施过程中。RTCA DO-160G 涉及多个学科的知识，包括航空、电子、电磁学和机械工程，因此需要确保使用的术语在所有学科中具有一致的定义。为了确保测试人员和工程师在执行测试时对所有条件和要求的理解一致，RTCA DO-160G 标准中定义了大量术语。环境应力：指的是设备在操作过程中可能遇到的外部物理或化学条件，包括温度、湿度、振动等。电磁干扰（EMI）
Ubuntu22.04安装ros（一键搞定）
然后发现可以一键安装，话不多说，直接终端输入如下指令。回车之后系统会自动安装ros。
斑马算法优化变分模态分解ZOA-VMD数字信号去噪（优化K值 alpha值）【含Matlab源码 4887期】
斑马算法优化变分模态分解ZOA-VMD数字信号去噪（优化K值 alpha值） 完整的代码，包运行；运行操作视频见优快云资源！适合小白！
Statistics and Machine Learning Toolbox
Statistics and Machine Learning Toolbox（带书签），MATLAB统计学习工具
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")
Probability and Statistics - 4th edition
Probability and Statistics - 4th edition
VB摄像头监控
该VB模块可对摄像头捕获的画面进行录像等，可在创建的窗口进行控制
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
订阅专栏 解锁全文
1308
扫一扫
