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# 开头''' 或 """ 包裹起来(头和尾都是 3 个)，单引号（'）与双引号（"）在 Python 中并无太大区别作用：
程序员最讨厌的 10 件事：0. 别人的代码不写注释。 1. 给自己的代码写注释
当初写这段代码的时候只有上帝和我知道它是干嘛的，现在只有上帝知道
注：print() 的作用是将填入的内容显示在 Console 中
1.默认每次显示后会换行（等价于按了一次回车，或者 \n），控制结尾的参数是 end。
print("Data")
print("whale")
Data whale
STM32在六轴机械臂实时控制中的选型与分层架构设计
嵌入式实时控制系统中，微控制器的选型直接决定运动控制精度与系统可靠性。其核心在于匹配确定性中断响应、低延迟外设协同与工业协议栈支持等硬性指标。STM32凭借Cortex-M内核的NVIC调度能力、成熟HAL库及CAN FD/高级定时器等专用外设，在机电一体化场景中成为伺服控制中枢的首选。相比ESP32等通用MCU，STM32在硬实时性、EMC认证生态和工业总线兼容性上具备不可替代优势。本文围绕GTB六轴机械臂项目，详解基于STM32F407的硬件抽象层配置、FreeRTOS任务调度、事件队列解耦及闭环控制数
安卓逆向实战：用Batch Apktool 3.8.0快速汉化APK的完整流程
本文详细解析了如何使用Batch Apktool 3.8.0进行安卓APK汉化的完整流程，包括环境配置、反编译、资源解析、深度汉化、重构建与签名优化等关键步骤。通过这款集成化逆向工具，开发者可以高效完成APK汉化，提升工作效率。
AWS入门实践-如何在AWS云上创建一个内外网隔离的生产环境
在 AWS 上建立一个内外网分离的生产环境,可以减少应用服务的暴露面，有效的保证你的应用服务器的安全。通常我们会将web应用放在外网的子网内，数据库服务器等放在内网的子网。我们将按照下图来部署动手实践环境，实现在public subnet的EC2虚拟机可以直接通过互联网可以访问，在Private subnet的EC2虚拟机不能通过互联网直接访问，但是却可以在内部来访问互联网安装应用等等。
ChatGPT搭配Claude 4写文献综述效果出奇的好！四步法直接写出逻辑清晰、层次分明的文献综述（附复制即用专业提示词）
请梳理【研究领域】中核心理论的发展演进过程，包括每一阶段理论的核心观点、提出背景与推动力，并指出理论演化中的关键节点或范式转变。请根据以下文献摘要内容，帮助我梳理该主题的研究趋势，包括：常见研究视角、研究方法的演变、研究对象的变化方向等。请根据以下研究空白与我已有的研究基础，帮我生成一个清晰的研究定位，包括：研究主题、研究对象、方法路径和理论框架。先要明确这个研究领域中一个主导或热门的理论，比如社会支持理论、认知负荷理论等，强调这个理论内部的不一致性、适用性争议，通过交代这⼀争议的背景，引发读者兴趣。
OpenClaw（Clawdbot/Moltbot）如何正确的配置浏览器，实现对浏览器的自动化操作功能（浏览器状态为running:false怎么办）
OpenClaw（Clawdbot/Moltbot）浏览器自动化功能无效？🤖 本文详细介绍如何解决这个问题🔧，让你的OpenClaw（Clawdbot/Moltbot）真正具备"睁眼看世界"的能力，支持网页搜索、点击操作和截图等功能 ✅
基于唤醒音频的目标说话人语音识别系统设计报告
摘要 本报告设计了一套基于唤醒音频的目标说话人语音识别系统，通过五级流水线架构（唤醒检测→声纹注册→语音分离→目标ASR→拒识决策）解决复杂声学场景下的目标说话人识别问题。系统整合ECAPA-TDNN声纹模型、Conv-TasNet语音分离网络和Whisper ASR引擎，在L20 GPU上实现高效端到端推理，支持-5dB~5dB SNR范围和0~100%重叠率的双人语音场景处理。系统在目标说话人CER、拒识率和推理效率等关键指标上均达到设计目标，为智能语音交互提供了有效的身份感知解决方案。
rk3568 Android12 默认授予预装app权限
默认授予预装应用权限可以避免用户在每次使用应用时都要重复同意权限请求。尽管在Android 12中，用户对权限的管理有了更高的控制权，但这也可能导致用户在频繁使用预装应用时感到繁琐和不必要的干扰。通过默认授权，减少了这种麻烦。一些厂商可能会根据其定制的Android系统（如Samsung的One UI、Xiaomi的MIUI）要求某些预装应用默认获得权限，以便应用能够提供厂商特定的功能。这也有助于设备厂商提供统一和稳定的服务，不至于因为权限问题导致某些功能无法使用。
Android Telephony主要模块及功能
Telephony的代码主要由五个模块组成 1.Telephony Frameworks 代码区域： frameworks/opt/telephony/ 其中主要的文件在如下目录 /frameworks/opt/telephony/src/java/com/android/internal/telephony/ 其中比较常见的有以下几类文件 1.BaseCommands、RIL构成R
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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
jre9下载 jdk9
(Java SE Runtime Environment 9.0) 9.0u175 官方最新版 【x86|x64】
ssm框架搭建
这个是一个在idea上成功跑起来的ssm框架项目，保存在这里方便以后使用直接下载
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