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告别双系统！用PhyFusion在麒麟V10上无缝融合Win11，文件互传、GPU加速全搞定
本文详细介绍了在飞腾D3000平台上使用PhyFusion实现麒麟V10与Win11无缝融合的解决方案。通过视觉无界、数据互通和性能无损的三大突破，PhyFusion显著提升了跨系统工作效率，支持GPU加速和文件互传，是国产化平台的革命性工具。
grafana 配置页面告警
Rate 下拉选择也可，保持默认也可。By label 选择一个比如选择filename。配置无数据或者错误数据的时候如何处理，这里选择 Normal （表示当做正常不会告警）单机 node_name 标签，选择一个主机，选好后单机 Show logs。单机 New floder，如果之前配置过，也可以选择。单机 Save contact point 保存即可。会跳出如下对话框（如果没有跳出就自己单机选择下）完成后会自动生成如下图，告警规则部分就配置好了。选择告警接收方式（这里选择企业微信 WeCon）
CRPR的三种关键影响：从SI、OCV到Min Pulse Width的时序优化实践
本文深入探讨了CRPR（时钟重汇聚悲观度消除）在芯片时序优化中的三种关键影响：信号完整性（SI）、片上变异（OCV）和最小脉宽检查。通过实际案例分析和技术细节，展示了CRPR如何有效修正时序分析中的过度保守估计，提升设计精度。文章特别强调了SI-aware CRPR的调试技巧和OCV补偿的最佳实践，为芯片设计工程师提供了宝贵的时序优化指导。
组合导航、惯导、GNSS、INS、IMU的区别
：惯性导航系统（INS）是一种利用安装在运载体上的陀螺仪和加速度计来测定运载体位置的系统。它通过测量物体的加速度和角速度，结合算法计算出物体的位置、速度和姿态12。。
基于STM32L4XX、HAL库的IP101GRI以太网收发器（PHY）驱动程序设计
define IP101GRI_PHY_ADDRESS 0x01 // 根据硬件设计设置PHY地址。#define IP101GRI_PHYSTS 0x10 // PHY状态寄存器。#define IP101GRI_PHYINIR 0x12 // PHY中断寄存器。#define IP101GRI_PHYCR 0x14 // PHY控制寄存器。#define IP101GRI_PHYID2 0x03 // PHY标识符2。
YOLO26 网络结构逐模块解析：Backbone/Neck/Head
本文解析了YOLO26目标检测网络的架构设计，重点介绍其在Backbone、Neck和Head三个核心组件的创新优化。Backbone采用改进的CSP模块与轻量级注意力机制，提升特征提取效率；Neck使用改进的BiFPN结构实现高效多尺度特征融合；Head采用完全解耦设计简化回归流程。文章详细分析了各模块的技术演进、实现细节和设计思想，通过流程图和代码示例展示了YOLO26的整体架构和关键组件实现。该研究对模型优化、定制开发和硬件部署具有重要指导意义。
SPI 总线信号完整性与时序一致性测试
SPI总线测试的核心目标是验证其电气与时序行为是否满足芯片规格要求，确保通信可靠性
UNet + Shuffle Attention：一种轻量高效的医学图像分割注意力机制
本文提出了一种轻量高效的医学图像分割方法，将Shuffle Attention模块嵌入UNet的上采样路径中。Shuffle Attention通过通道分组注意力机制增强关键特征，同时保持计算轻量化。该方法在上采样后应用注意力模块，有效融合高层语义与低层细节，计算开销小且易于部署。实验表明，该模型在保持UNet原有优势的基础上，通过注意力机制提升了特征聚焦能力，适用于多种图像分割任务。未来可进一步结合空间注意力或Transformer模块以增强模型性能。
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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")
IEM模型，地表粗糙度模拟
利用微波反演土壤水分时候，利用IEM模型可以实现对随机地表的模拟
OV9734 DATASHEET，完整规格书
OV9734完整规格书，包括电气参数，尺寸规格，寄存器配置表等
TMS_WebGMaps_1.9.8.0_Full_Source
TMS_WebGMaps_1.9.8.0_Full_Source 全源码 delphi cbuilder
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