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当在Windows 10上播放视频时遇到声音卡顿问题时，可以通过编程来尝试解决。在本文中，我将介绍一种可能的解决方案，其中包括一些示例代码。
在开始编程之前，我们需要明确问题的原因。声音卡顿可能是由于计算机的性能问题或音频驱动程序的不兼容性引起的。下面是一些解决方法，可以通过编程来尝试修复这个问题。
import psutil
cpu_percent = psutil.cpu_percent()
memory_usage = psutil.virtual_memory().percent
音视频开发之旅（66) - 音频变速不变调的原理
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彻底解决Windows播放HEVC视频无画面错误0xc00d5212的四种方法
视频编解码器是数字媒体处理的核心组件，负责对视频数据进行压缩与解压缩。其工作原理是通过特定算法减少数据量，以实现高效存储与传输。在多媒体技术生态中，编解码器的兼容性直接影响播放体验与开发效率。HEVC（高效视频编码，即H.265）作为新一代主流编码标准，因其出色的压缩率而被广泛应用于4K/8K超高清视频、流媒体服务及专业影视制作。然而，Windows系统原生并未完全集成HEVC解码器，导致用户播放相关视频时频繁遭遇仅有声音而无画面的典型故障，并常伴随“0xc00d5212”错误代码。本文聚焦于这一高频技术痛
从零开始学华为设备：手把手教你用ENSP搭建OSPF实验环境（附拓扑文件）
本文详细介绍了如何使用华为ENSP模拟器从零开始搭建OSPF多区域网络实验环境，包括拓扑规划、设备配置、OSPF参数优化及故障排查技巧。通过实战案例和配置示例，帮助网络工程师快速掌握华为设备的OSPF协议配置，提升企业网络环境中的实战能力。
DELL服务器BIOS下配置RAID阵列
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ArcGIS新手必看：HWSD2土壤数据从下载到批量提取TIFF的完整流程
本文详细介绍了ArcGIS中HWSD2土壤数据的完整处理流程，从数据下载、预处理到批量提取TIFF文件。涵盖栅格属性表构建、属性数据连接及性能优化等关键步骤，帮助新手高效掌握HWSD2数据提取技术，提升地理信息数据处理效率。
SAP 基于生产订单行项目外部加工创建采购申请时的一个增强需求
摘要 本文介绍在SAP系统中实现生产订单外协工序自动生成采购申请(PR)时，将指定文本写入PR抬头文本的技术方案。关键点包括： 业务背景：通过CO02事务码将工序标记为外部加工，系统自动生成PR，需在生成时自动写入抬头文本。 技术难点： PR抬头文本存储在STXH/STXL而非EBAN表 标准增强点ME_PROCESS_REQ_CUST在外协场景可能不触发 推荐方案： 使用BAdI ME_REQ_POSTED，在PR过账时点执行 通过Text Object EBANH和Text ID(如B01)写入文本 实
三种方法教你下载大模型到本地，总有一种适合你
本文主要介绍了下载大模型到本地的三种解决方案，希望能对同学们有所帮助。 文章目录 1. 场景/问题描述 2. 方案一：使用git 3. 方案二：镜像站 4. 方案三：高速下载工具（变体及自定义代码） 4.1 aria2 4.2 自定义代码
【QNX+Android虚拟化方案】129 - USB眼图参数配置
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CPD (Coherent Point Drift) 算法实现点云配准 C++版
作为和icp并列的点集配准算法cpd为什么没有icp火，等我把整个方案跑完才明白。 首先下载cpd https://github.com/gadomski/cpd 包含的fgt https://github.com/gadomski/fgt 下载到win10的解压后 运行cmake来进行编译 这个时候会提醒缺少eigen3 fgt python 这几个都安装排除后 在build文件夹下会生成vs2017的源码，右键全部生成后在 .\cpd-master\build\Debug 下会生成一个cpd.li
YOLO26创新改进 | 全网独家创新篇、小目标检测专属 | AAAI 2025 | 引入HS-FPN中的HFP和SDP创新点，从频域增强小目标特征，淘汰FPN进行升级，助力YOLO26有效涨点
本文提出HS-FPN特征金字塔网络，通过高频感知模块(HFP)和空间依赖感知模块(SDP)优化YOLO26模型。HFP利用高通滤波器增强小目标的高频特征，SDP捕获相邻像素的空间依赖关系。在AI-TOD等小目标检测数据集上，HS-FPN相比传统FPN显著提升性能。文章详细介绍了HFP和SDP的模块结构、核心代码实现，以及如何在YOLO26中集成这两个模块的配置方法，提供了多个版本的yaml文件配置方案。实验结果表明该方法能有效改善微小目标检测效果。
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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")
C# WinForm 基于DirectShow.dll编写的程序，可边预览边保存视频，音频和图片
基于微软的DirectShow.dll的二次开发1.自动识别连接的摄像头跟麦克风且可以对其进行选择（默认选中第一个）2.可录制wmv和avi两种格式的视频并且都带有音频，avi可以录制的分辨率和帧数进行选择，wmv默认进行了淸晰度优化如不喜欢可去除代码中有相关提示3.可在预览点开之后进行拍照，也可边录制视频边拍照4.添加了视频和音频的压缩功能（酌情使用）这次二次开发发现C#对DirectShow的相关信息太少，这次做完也不敢藏私共享出来，还望大家指正
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