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本文介绍Hype-YOLO提出的MANet模块，作为C6/C2f/C3k2的升级结构，融合深度可分离卷积（DSConv）与多分支ConvNeck设计，强化特征表达与梯度流动。通过替换YOLOv11主干中的C3k2模块，显著提升语义深度与检测精度。文中详述MANet原理、PyTorch实现、Ultralytics框架下的四步集成方法及yaml配置与训练流程。
本文给大家带来的最新改进机制是由Hype-YOLO提出的MANet结构，其是一种C3、C2f、C3k2的升级版，用于增强特征提取能力。MANet可以理解为C2f的升级版，其整合了DSConv和C2f结构，这些模块的组合使得信息流动更加丰富，有助于在训练过程中提高梯度流动，从而增加基础特征的语义深度。官方发布的数据中HypeYOLO的精度是高于YOLOv11的，本文内容包含MAnet替换C3k2的教程以及原理介绍。
欢迎大家订阅我的专栏一起学习YOLO，购买专栏读者联系读者入群获取进阶项目文件！
（文字学不会的读者，作者可提供视频学习方法.）
专栏目录（更多改进详见）：YOLOv11改进系列专栏目录——本专栏持续复习各种顶会内容——上百种最新改进机制
目录
论文链接：官方论文链接点击此处即可跳转
代码链接：官方代码链接点击此处即可跳转
YOLOv8改进有效系列目录 | 包含卷积、主干、检测头、注意力机制、Neck上百种创新机制
Hello，各位读者们好，本专栏自开设两个月以来已经更新改进教程120+余篇其中包含C2f、主干、检测头、注意力机制、Neck多种结构上创新，也有损失函数和一些细节点上的创新，订阅本专栏以后你不仅可以收获跟专栏的阅读权限，同时可以进Qq群，里面包含集成我所有创新的YOLO最新目录，和我本人录制的视频讲解教程，如果你想要在YOLOv8系列收获一篇论文，我相信订阅本专栏后你一定会有所收获~YOLOv8改进有效系列目录。
YOLOv11改进有效涨点专栏目录 | 含卷积、主干、注意力机制、Neck、检测头、损失函数、二次创新C2PSA/C3k2等各种网络结构改进
Hello，各位读者们好.本文为最新YOLOv11有效涨点专栏目录，YOLOv11以及发布了一个月左右，这个过程中我也是给大家整理了许多的机制，其中包含了C3k2、主干（均支持根据yolov11训练的版本进行二次缩放，全系列都能轻量化）、检测头、注意力机制、Neck多种结构上创新，也有损失函数和一些细节点上的创新。
YOLOv11 改进 - 特征融合 | 融合Hyper-YOLO混合聚合网络MANet（Mixed Aggregation Network）通过多路径设计实现高效特征学习与模型适应性提升
本文介绍了新型目标检测方法 Hyper - YOLO 及其在 YOLOv11 中的结合。传统 YOLO 模型颈部设计有局限，为此提出超图计算驱动的语义收集与扩散框架（HGCSCS），将视觉特征图转化到语义空间并构建高阶信息传播机制。Hyper - YOLO 在主干网络引入混合聚合网络（MANet），结合多种卷积模块增强特征提取能力；在颈部网络设计基于超图的跨层级与跨位置表示网络（HyperC2Net）支持高阶交互。
YOLO26优化：注意力魔改 | 归一化的注意力模块（NAM）,高效且轻量级的注意力机制
NAM作为一种高效且轻量级的注意力机制。采用了CBAM的模块集成并重新设计了通道和空间注意子模块
YOLOv10优化：neck优化 | Hyper-YOLO创新点（HyperC2Net+MANet）助力YOLO，实现二次创新
Hyper-YOLO在其骨干网络中引入了提出的混合聚合网络（MANet）以增强特征提取，并在其颈部引入了基于超图的跨层级和跨位置表示网络（HyperC2Net）
Nordic nRF52832实战：手把手教你搭建智能手环BLE通信框架（附完整代码）
本文详细介绍了如何使用Nordic nRF52832芯片搭建智能手环的BLE通信框架，涵盖芯片优势、协议栈初始化、低功耗广播策略及健康数据服务实现。通过完整代码示例和架构设计，帮助开发者快速掌握蓝牙低功耗通信技术，适用于可穿戴设备开发。
Verilog-asm小练习—PLL建模
Verilog- ams是Verilog标准的模拟混合信号语言，以下是对PLL建模的一种思路参考
IBM AC922 8335-GTW 安装linux，CentOS，ubuntu，RHEL各种坑
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【安全设备篇】HVV面试专题
还有就是这个可能是被访问的这个内网系统，本身业务系统存在业务逻辑问题，因为内网所布置的很多业务系统往往不会考虑一些安全性问题，比如在正常的业务逻辑请求中直接带了sql语句，就会触发设备规则产生告警，这一类是属于误报，然后会将此告警上报给研判人员，需要研判组专家和甲方客户确认下是否是该系统存在此业务逻辑问题，并且需要确认告警的源ip在此告警的时间段附近有没有进行操作过。天眼日志分析主要基于系统和应用程序的日志数据来识别安全威胁，而威胁感知则通过收集和分析多种来源的情报信息。理解威胁的性质和威胁背后的原理。
西门子SMART PLC增量式PID(完整梯形图FC)
西门子PLC自带的PID指令是基于位置型的PID，位置型PID的算法公式和详细介绍，大家可以参看《PID专栏》的其它文章，这里不再赘述。本篇博文直接给出代码部分的讲解，不设计太多公式和理论的推导。限于本人能力，文中难免出现错误和不足之处，诚恳欢迎大家的批评和指正。同时感谢大家的订阅和关注。SMART200 PLC系统自带的PID指令解析，可以参看下面这篇文章：SMART PLC PID算法基本解析（附公式）_RXXW_Dor的博客-优快云博客_plc的pid算法在态运行中，控制器调节输出值，使偏差 (e
中国农业银行——轻量式云原生应用平台（轻云平台）
2021年10月，中国人民银行等联合发布了《关于规范金融业开源技术应用与发展的意见》（银办发〔2021〕146 号），规范金融机构合理应用开源技术，提高应用水平和自主可控能力，促进开源技术健康可持续发展。前期，为助力成员单位做好开源技术应用与发展工作，北京金融科技产业联盟开源专委会组织了金融业开源技术应用、创新等方面的案例征集，现对部分优秀案例进行宣传，发挥先进典型示范引领作用。申报单位：中国农业银行股份有限公司技术领域：云原生、无服务器架构（Serverless）、DevOps。
STC单片机使用FatFs文件系统读取SD卡指定文件内容
STC单片机使用FatFs文件系统读取SD卡指定文件内容
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
Probability and Statistics - 4th edition
Probability and Statistics - 4th edition
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")
Camera2Demo
在Camera2 的基础上进行封装，使得Camera2一堆初始化逻辑与Activity分离，降低耦合度，完成预览拍照并保存的操作
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