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问题1：来自算法竞赛入门6.1
某城市有一个火车站，铁轨铺设如图6-1所示。有n节车厢从A方向驶入车站，按进站顺序编号为1~n。你的任务是让它们按照某种特定的顺序进入B方向的铁轨并驶出车站。为了重组车厢，你可以借助中转站C。这是一个可以停放任意多节车厢的车站，但由于末端封顶，驶入C的车厢必须按照相反的顺序驶出C。对于每个车厢，一旦从A移入C，就不能再回到A了；一旦从C移入B就不能再回到C了。换句话说，在任意时刻，只有两种选择：A->C和C->B
样例输入：
5
1 2 3 4 5
5
5 4 1 2 3
6
654321
样例输出：
Yes
No
列车调度程序 利用栈
利用栈写的一个列车调度程序，是严蔚敏教材的课程设计，仅供参考。
火车通过一个栈式的列车调度站
火车通过一个栈式的列车调度站，可能得到的调度结果有哪些？如果 有n 列火车通过调度站，请设计一个算法，输出所有可能的调度结果。
【首份】2023年30米分辨率土地利用遥感监测数据
为了恢复和重建我国土地利用变化的现代过程，更好地预测、预报土地利用变化趋势，地球资源数据云以美国陆地卫星Landsat 遥感影像数据作为主信息源，结合Sentinel系列数据，通过人工目视解译和机器学习方法，建立了国家尺度1：10 万比例尺多时期土地利用/土地覆盖遥感监测数据库（CNLUCC）因早期Landsat数据时空覆盖缺失，1972-1977年合成第一期数据，1978-1985年合成第二期数据，1986年起每年度生成一期数据。投影：Albers正轴等面积双标准纬线圆锥投影南标准纬线：25°N。
libero烧录信号抓取
提示:如果改了东西需要重新烧录，需要把这两个界面都关闭再libero打开。两个红色的要关闭掉。然后返回libero开始烧录，观测信号（如果只烧录观测从这一步开始就行）等到下面的messige出现以下信息表示run好了。在下图中右侧可以自己设置，设置完之后记得保存一下。run一下，耐心等待，有警告就ok，run完之后。run成功会变绿色，然后点下面的这个看波形。设置抓取信号:在libero打开。打开的就是烧录界面，直接run。
SikuliX实战：5分钟搞定游戏挂机脚本（附防检测思路与Python集成技巧）
本文详细介绍了如何使用SikuliX快速开发游戏挂机脚本，包括环境配置、基础原理、防检测策略及Python集成技巧。通过实战案例展示如何实现自动化任务、高级防检测方案，并结合Python提升脚本性能与智能度，帮助玩家高效完成重复性游戏操作。
车载以太网测试技术解析
在智能汽车与HiL测试领域，高效解析与模拟车载网络通信是开发验证的关键。ZD-Converter 2000以媒体/介质转换器、嗅探器、拦截器、仿真器四合一的颠覆性设计，成为工程师应对复杂车载以太网测试的瑞士军刀。它支持双路车载/标准以太网智能转换、实时通信嗅探、硬件级数据拦截及ECU通信模拟，更通过串口命令实现全参数远程配置，赋能自动化测试流程。本文将深度解析其核心功能与实战应用场景。
华为ENSP虚拟网络与本地物理网卡高效桥接实战指南
本文提供了一份详细的华为ENSP虚拟网络与本地物理网卡桥接实战指南。通过清晰的四步操作，帮助用户打破虚拟实验环境的“孤岛”限制，实现ENSP设备与真实局域网及互联网的互通，从而进行更真实的协议测试、软件升级和混合网络环境搭建。
STM32F407 CubeMX UART DMA高效解析BNO085九轴数据
本文详细介绍了如何在STM32F407平台上使用CubeMX配置UART和DMA高效解析BNO085九轴传感器数据。通过DMA循环接收机制实现低CPU占用的实时数据采集，并分享数据解析技巧、错误处理及实战调试经验，为嵌入式开发者提供完整解决方案。
【国产MCU】-CH32V307-通用定时器（GPTM）-输入捕获模式测量脉冲
CH32V307的通用定时器模块包含一个16 位可自动重装的定时器（TIM2、TIM3、TIM4和TIM5），用于测量脉冲宽度或者产生特定频率的脉冲、PWM波等。可用于自动化控制、电源等领域。 输入捕获是通用定时器功能之一，一般用于测量频率和测量脉冲宽度。本文将详细介绍如何通用定时器的输入捕获功能。
【软件系统架构】系列十一：面向对象技术—sysML
摘要： SysML（系统建模语言）是UML的扩展，专为复杂系统工程设计，支持硬件、软件、人员等跨领域建模。其核心包括9种图：4种结构图（如块定义图BDD、内部块图IBD）、4种行为图（如活动图AD、序列图SD）和需求图（REQ），覆盖需求、结构、行为及参数约束。相比UML（面向软件），SysML精简图表（9 vs 13），强化需求追踪与性能分析（如参数图PD），并支持MBSE（基于模型的系统工程）。需求关系（如满足、验证、派生）在需求图中明确关联设计实现，形成闭环管理。SysML是UML的子集与系统工程扩展
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
Devexpress16.2.6破解工具
Devexpress16.2.6破解工具Devexpress16.2.6破解工具Devexpress16.2.6破解工具Devexpress16.2.6破解工具
精雕软件(JDpaint) 5.5 绿色版
精雕软件(JDpaint) 5.5 绿色版精雕软件(JDpaint) 5.5 绿色版精雕软件(JDpaint) 5.5 绿色版
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")
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