1 //电子表的设计包括正常计时模块,LED显示模块,定时报警模块,校时模块,秒表模块。 2 //(1)正常计时模块clock 3 module clock(clk,rst,clock_en,second,minute,hour); 4 input clk,rst,clock_en; 5 output[5:0]second,minute,hour; 6 reg[5:0]second,minute,hour; 7 always@(posedge clk or negedge rst or posedge clock_en) 8 if(!rst) 9 begin 10 second<=0; minute<=0; hour<=0; 11 end 12 else if(clock_en) 13 begin 14 if(second==59) 15 begin 16 minute<=minute+1; 17 second<=0; 18 end 19 else if(minute==59) 20 begin 21 hour<=hour+1; 22 minute<=0; 23 end 24 else if(hour==23) 25 hour<=0; 26 else 27 second<=second+1; 28 end 29 endmodule
30 //(2)LED显示模块LED_display 31 module LED_display(data,high,low); 32 input[5:0]data; 33 output[7:0]high,low; 34 reg[7:0]high,low; 35 reg[3:0]out_h,out_l; 36 always@(data) 37 begin 38 out_h<=data/10; 39 out_l<=data; 40 end 41 always@(out_l) 42 case(out_l) 43 4'b0000:low<=8'b00000000; 44 4'b0001:low<=8'b01100000; 45 4'b0010:low<=8'b11011010; 46 4'b0011:low<=8'b11110010; 47 4'b0100:low<=8'b01100110; 48 4'b0101:low<=8'b10110110; 49 4'b0110:low<=8'b10111110; 50 4'b0111:low<=8'b11100000; 51 4'b1000:low<=8'b11111110; 52 4'b1001:low<=8'b11110110; 53 default:low<=8'b00000000; 54 endcase 55 56 always@(out_h) 57 case(out_h) 58 4'b0000:high<=8'b00000000; 59 4'b0001:high<=8'b01100000; 60 4'b0010:high<=8'b11011010; 61 4'
相关推荐
基于Verilog的多功能电子表
基于Verilog的多功能电子表,文本打开.v文件,配置引脚后测试
基于Verilog的电子表
基于Verilog的电子表。具有闹钟和显示当前时间的功能,时分秒。可通过按键调节时间。代码附较为详细的注释
Verilog电子表编程实例
电子表Verilog编程 多功能电子表共有5种功能:功能1为数字钟;功能2为数字跑表;功能3为调时;功能4为闹钟设置;功能5为日期设置。除调时功能以外,电子表处于其他功能状态下时并不影响数字钟的运行。使用数字钟功能时,还可以通过按键快速查看当前的闹钟设置时间和当前日期。该电子表利用EDA实验平台的扬声器整点报时和定时报时,设置3个按键分别作为功能键和调整键。
用于FPGA的Verilog语言的电子表
用于FPGA的Verilog语言的电子表 内涵引脚图
verilog语言描述的电子表
用verilog语言描述的计数器,可用以电子表的设计,有六进制和十进制的计数器,有24进制的计数器
FPGA实现电子表verilog
利用FPGA开发板实现的电子表功能;初始默认状态显示时间,具有闹钟提示功能;可以倒计时,并且可修改倒计时时间;具有多通道计时器功能,计时完成后可以查看计时记录。针通过vivado开发软件设计,并于bsay3 等多款开发板上测试应用。
Ansys Lumerical 2023 R1.2新功能实战:薄膜铌酸锂调制器仿真全流程解析
本文详细解析了Ansys Lumerical 2023 R1.2在薄膜铌酸锂电光相位调制器仿真中的最新功能与应用。通过实战演示,展示了从电极配置、电场计算到模式分析的全流程,重点介绍了各向异性介电常数支持和多物理场耦合优化等关键技术,助力开发者高效设计高性能光通信器件。
win11一直弹出用户账户控制怎么解决
用户帐户控制简称UAC,是微软公司在其Windows Vista及更高版本操作系统中采用的一种控制机制。近期有部分Win11用户想要关闭UAC,但是不知道应该如何操作,对此下面我就教给大家操作方法,大家可以去尝试看看。...
VCL for PHP,Konopka Signature VCL Controls (Raize Components 6.2.3) for Delphi 10.4
The Raize Components suite includes a variety of interface design tools for Delphi programming language and C ++ Builder, which includes over 125 VCL controls and over 100 different tools for creating...
优雅谈大模型:“System2”与“System 1”
研究人员探索了三种System 2方法——RaR、S2A 和 BSM,这些方法已经成功提炼到新的LLM,这个新的模型与System 1相比,产生了更好的结果,而且成本低于System 2。然而这种方法是有局限性,只能适用于特定的任务。
2026.11.12:2026.11.12最新的Ubuntu26.04安装docker-ce
本文介绍了在Ubuntu 26.04系统上安装Docker CE的完整步骤。首先需要卸载旧版本的Docker组件,然后通过设置官方仓库进行安装。具体步骤包括:更新系统、安装必要依赖、添加Docker GPG密钥、配置稳定版仓库源、安装Docker Engine及containerd。文章还提供了安装特定版本Docker的方法,并说明了如何将用户加入docker组以获取权限,最后设置Docker开机自启。整个过程详细清晰,适合在Ubuntu系统上部署Docker环境的用户参考。
YOLO26跨时代升级!YOLO26 MLCA注意力机制解读
摘要: 本文提出了一种基于多级上下文聚合(MLCA)注意力机制的改进卷积神经网络。MLCA模块通过局部和全局注意力机制动态调整特征图权重,结合了ECA注意力与自适应池化,显著提升特征表达能力。实验表明,添加MLCA后模型mAP提升12.11,准确率提高5.43。文中详细解析了MLCA1模块的工作原理,包括局部/全局池化、卷积操作和注意力加权机制,并介绍了集成MLCA的Bottleneck_MLCA模块。该机制通过多层次特征融合,在保持计算效率的同时增强模型对关键特征的敏感性。最后提供了YOLOv26-MLC
23、工业物流中的多智能体路径规划与碰撞处理策略
本文探讨了工业4.0背景下工业物流中的多智能体路径规划(MAPF)与碰撞处理策略。分析了自动导引车(AGV)等移动资源的应用现状,综述了主流路径规划算法如A*、D*Lite、M*等,以及集中式与分布式碰撞检测方法。重点比较了五种碰撞避免策略:路径交叉、优先级、起始时间调整、无交叉路径和速度调整,提出了基于环境条件的策略选择流程,并通过流程图展示了评估、组合、测试与优化的全过程。文章强调应根据订单不确定性、时间约束、优先级和生产组织等因素综合选择或组合策略,以提升物流效率与系统可靠性。
论文阅读:NeurIPS 2024 JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Model
论文指出评估越狱攻击存在挑战,如缺乏评估标准、成本和成功率计算方法不一致、许多研究无法复现。为解决这些问题,作者提出了JailbreakBench,这是一个开源基准测试,包含以下组件:持续更新的越狱提示库(jailbreak artifacts)。包含100种行为的越狱数据集,符合OpenAI的使用政策。标准化的评估框架,包括威胁模型、系统提示、聊天模板和评分函数。跟踪各种LLMs攻击和防御性能的排行榜。
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")
WINCC7.3破解文件
WINCC7.3硬件够破解文件,有需要的下载!WINCC7.3硬件够破解文件,有需要的下载!
VC中sqlite的使用
在VC中对sqlite的使用,实现数据库大批数据的快速插入,读取数据库中的数据在ListCtrl中显示
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
1213



被折叠的 条评论
为什么被折叠?



