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题目要求:用ARM汇编语言实现1~100之间之和（5050 = 0x13BA）
.text @声明以下内容是文本段的内容
.global _start @.global声明_start标签是一个全局标签
_start:
mov r1,#0x0 @r1 = sum
mov r2,#0x1 @r2 = i
fun: @加法函数
cmp r2,#100 @r2中的值和100作比较
add r1,r1,r2 @ sum = sum + i
add r2,r2,#1 @ i = i + 1
blt fun @如果r2 < 100 则跳转到fun:
loop:
b loop
.end
Comsol 管道导波裂纹检测：从建模到求解的完整流程解析
本文详细解析了使用Comsol Multiphysics进行管道导波裂纹检测的完整仿真流程。从几何建模、材料定义、物理场设置到网格划分与求解后处理，系统阐述了如何构建高保真虚拟模型以模拟导波与裂纹的相互作用。重点分享了激励信号设置、频散效应处理及参数化扫描等实战技巧，旨在帮助工程师与研究人员高效优化无损检测方案，降低实验成本。
arm keil day2 求最大公约数和1~100的和
arm keil day2 求最大公约数和1~100的和
Cadence Allegro新手避坑：从原理图到PCB，解决‘netrev.lst does not exist’的完整流程
本文详细介绍了Cadence Allegro从原理图设计到PCB导入网表的完整流程，特别针对新手常见的'netrev.lst does not exist'错误提供了系统解决方案。内容包括环境准备、原理图设计规范、网表生成与导入技巧，以及设计数据管理最佳实践，帮助工程师高效完成PCB设计任务。
day2 ARM基础
*指定.bss段的起始位置*/ .bss : { *(.bss) } __bss_end__ = .;/* 指定.data段的起始位置*/ }.text .global _start _start: mov r0,#0XFF and r1,r0,#(~(0X1
STM32 伺服电机 指令脉冲+方向位置控制模式 主从定时器 实现PWM脉冲数精确控制
实验室有个项目涉及到多个步进电机以及伺服电机的控制，需要电机得到一个触发信号后精确移动一段距离。 下面以单个伺服电机控制为例，利用伺服电机的指令脉冲加方向位置控制模式实现jing'que ...
取证实战：当嫌疑人电脑已关机，如何利用EFDD从休眠文件提取BitLocker密钥？
本文详细介绍了如何利用Elcomsoft Forensic Disk Decryptor(EFDD)从休眠文件(hiberfil.sys)中提取BitLocker密钥的实战技术。通过分析休眠文件中的内存快照，取证人员可以突破已关机加密设备的防线，获取关键解密信息。文章涵盖了工具配置、镜像获取、密钥提取及验证等完整流程，并提供了疑难问题解决方案和法律合规建议。
arm day1
2.用for循环实现1~100之间的和 13BA for(i=1;
Allegro File菜单实战指南：从导入网表到导出BOM的完整流程
PCB设计离不开对EDA软件菜单体系的深入理解。在Cadence Allegro中，File菜单是数据流转的中枢，它不仅是新建、打开、保存的基础入口，更是连接原理图与版图、设计与制造的关键桥梁。工程师常遇到的“导入网表报错”“导出BOM为空”“板框不闭合”等高频问题，大多源于对File菜单中Import与Export功能的理解不到位。从基础概念出发，先厘清Import（网表、DXF、IPC）与Export（BOM、ODB++、DXF）的工作原理，再结合版本兼容、封装库路径、单位设置等工程细节，即可构建完整的
ARM day2 作业
ARM day2 作业
2024年数字IC秋招-格科微-数字电路设计工程师-笔试题
笔试题型：简答题（9个）笔试平台：牛客（电脑监控）笔试时间：2h。
【音视频 ffmpeg 学习】 RTMP推流 mp4文件
3.当RTMP协议在互联网中传输数据的时候，消息会被拆分成更小的单元，称为消息块（Chunk）。1.RTMP(实时消息传输协议)是Adobe 公司开发的一个基于TCP的应用层协议。2.RTMP协议中基本的数据单元称为消息（Message）。下载地址：http://nginx.org/download。下载 rtmp模块 到nginx 模块下。安装nginx 和 rtmp模块。（1）. linux 环境准备。下载nginx安装包。使用 vcl 播放流。
【ARM】Day2 汇编指令
作业1：思维导图 作业2： 作业3：用for循环实现1~100之间和5050
DICOM标准：DICOM医学影像中的覆盖层（Overlay）概念详解
DICOM标准：深入详解DICOM医学影像中的覆盖层（Overlay）
ARM day2作业
华清远见上海中心22071班 10.18作业
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
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")
day2 arm
用汇编for循环实现1~100之间的和。
汉书HE8，好用的汇编代码编写工具
汉书HE8，好用的汇编代码编写工具早期编写汇编代码所用功能强大
ARM-底层/Day2
ARM汇编指令
visio安装包
Microsoft Office Visio 是一款专业的制图软件。利用经过更新的形状、协作工具和数据链接图表，创建专业级图表以简化复杂的信息。
ARM DAY2
华清远见上海中心22071班
keil-＞汇编-＞汇编指令-＞day2
汇编指令
ARM day3
1.求两个数的最大公约数 2.用循环实现1~100的相加和 3.从SVC模式切换到User模式简单写法
ARM架构(Day2)
作业1： 作业2：用汇编for循环实现1~100之间的和 作业3：
ARM 汇编 day2
【代码】ARM 汇编 day2。
all-MiniLM-L6-v2轻量模型实测：ARM64架构Mac M1/M2芯片原生运行无兼容问题
本文介绍了如何在星图GPU平台上自动化部署all-MiniLM-L6-v2轻量级句子嵌入模型，实现高效的文本语义表示。该模型支持ARM64架构原生运行，适用于文档检索、内容推荐和相似度计算等场景，显著提升自然语言处理任务的效率。
3.21 arm Day2
汇编指令
ARM day10
消除随机噪声：单次采样易受干扰，多次采样平均后更稳定降低瞬时波动：尤其光敏电阻输出受环境光抖动影响较大提升有效位数：N 次平均，理论信噪比提升，100 次约提升20 dBADC（Analog-to-Digital Converter，模数转换器），是一种将连续的模拟信号转换为离散的数字信号的电子器件。逐次逼近法 SAR（Successive Approximation Register）是目前应用最广泛的 ADC 转换方法之一。其核心思想是：用二分法的思想，从最高位（MSB）开始逐位试探，通过比较器。
Day05_ARM裸机
(Dynamic RAM)动态RAM，DRAM只能将数据保存很短的时间，为保持数据，DRAM使用电存储，所以必须隔一段时间refresh一次，如果存储单元没有被刷新，存储的信息就会丢失。：(Static RAM)静态RAM，SRAM只要通电，里边存储的数据就可以恒常保持。掉电不擦除，norfalsh（电擦写）是rom（不可改写）的升级版。集成度低（单位面积存储单元稀疏），耗电高，读写极快，成本高。集成度高，耗电中（需时钟同步），读写一般，成本低。集成度高，耗电低，读写一般，成本低。
底层day2作业
华清远见22071班作业
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