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The hacker playbook3 完整版
The hacker playbook3 完整版Back for the third season, The Hacker Playbook 3 (THP3) takes your offensive game to the pro tier. With a combination of new strategies, attacks, exploits, tips and tricks, you will be able to put yourself in the center of the action toward victory
- 2020-12-07下载
- 积分：1
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Scratch 嫦娥奔月（初始）.sb2
Scratch 嫦娥奔月（初始）.sb2
- 2020-11-30下载
- 积分：1
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基于stm32和hx711的压力传感器代码
单片机是STM32F103ZET6，结合HX711读取压力传感器传输进来的压力值，利用串口送到PC显示
- 2020-06-17下载
- 积分：1
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PRBS信号进行QPSK调制；画出星座图；加入噪声后计算误码率
通信原理课程习题：1.以2^10个二进制PRBS作为消息码，载波频率为2.4GHz，利用MATLAB画出QPSK调制波形的频谱图和星座图，其中码元速率为50MBaud/s2.在上述生成的QPSK信号中引入高斯白噪声，调节SNR至10dB（或者其他认为合适的值）然后重新生成星座图，并计算误码率。（选做）
- 2020-12-01下载
- 积分：1
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C#编写《LED圆形灯》控件
使用c#编写的LED小灯控件，可以使用在工业控制中。开发环境：Visual Studio 2010开发语言：C#
- 2020-12-06下载
- 积分：1
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单点定位MATLAB程序源码
卫星观测文件Rinex读取和计算单点定位结果的文件，适合GPS BDS系统
- 2020-11-28下载
- 积分：1
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1.3寸OLED带字库（全套资料及各种单片机例子）
1.3寸OLED带字库（）1 概述 GT20L16S1Y是一款内含15X16点阵的汉字库芯片，支持GB2312国标简体汉字（含有国家信标委合法授 权）、ASCII字符。排列格式为竖置横排。用户通过字符内码，利用本手册提供的方法计算出该字符点阵在 芯片中的地址，可从该地址连续读出字符点阵信息。 1.1 芯片特点 ● 数据总线：SPI 串行总线接口 ● 点阵排列方式：字节竖置横排 ● 时钟频率：30MHz(max.) @3.3V ● 工作电压：2.2V~3.6V ● 电流： 工作电流：8mA 待机电流：8uA ● 封装：SOT23-6 ● 尺寸 SOT23-6：2.9mmX1.6 mm
- 2020-12-10下载
- 积分：1
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基于LMS和RLS的自适应滤波器的应用仿真
可以用于信号处理算法等大作业，用matlab编程实现LMS和RLS的自适应滤波器，并有实验结果
- 2020-12-07下载
- 积分：1
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计算机组成原理期末复习重点总结
本文档适用于大学生学习。主要归纳了计算机组成原理该科目的知识内容，用于期末复习。其中，部分资料为手写归纳，若出现错误请以课本为主星章对奴斜加迹一子电叶尊加亠究漏收电寻对水第代电陪引机第m大现真电计率五代善适计计尊机下分大型和E型和(高座能计加,起邻机,机。微望机22站图形强入利讨机(克入宣内研为時定方用温计专甲加养钝沉言顶程方一五佛。序南假记一可混言→机机费丸罢概描系力輒他指友)计尊加系览的是指附能够程序所四列计机系属池,可根点1结构与西能筋性计摩個氏是指加何突观对加构阿角属指与现机属性)仔夏计導机塔点)消金据存子鸦内,可挖地址形司3),指加椒推均甲二进劇表京)满由操市吊和她(源体脱粒位)今有備内把序悟該4)以泛摩题为中/↓现以有儒为)分鼻机餐很计而叫大烟章事件伦原Am从定伴:如快菜部伴执行速度所兼得秦瓷性庭加速比受限子试評件在系瓷中所占际重号性系涵能(、执行时同染进前)糸瓶如座比=系铙能品前)机行时间(后驶同(5)洪时时的1)x[(进例计+到评戈故行间前→系能加化代应司(下)可设进比例十的足越兼得而益程序而局部性原理时国和皇司)席章A基孤2|105余52(105)。=(11o|o1)212从般×→取坐0,80≤2原0XX≤为为真为整际应都0十原:L+0原二0,0000000-01原二1,0o0oco0模子益武而临界值边M)m整为补福:了30时82=x.[+0=[XD时[补=A+=从=A:)例:x=10)[2+=管位X=-1101=20=1000000直宦原码装换白科:了有为距模=有愿三8反:正码=嚓真负N反:原除磨号取反,原:-127~121)~8路-18~1补二十病而表示:鹃为x-(1)=真鱼万=真值例:像评单精度示701a.0-1010(·0|=·01089 ol01 t ollI Il- 1o0o olo△傅鸡宜格值制正殺原函0原硝0左病0升B右反A+8]补=1十工+补3号励态A-B]料三[A+TB补书两位3查利断?0=0直播乘该A例求解101x来礼点A0n+ oQ CoM初鱼0000A←A+Ao101鸡。0(00计鸦,。。010AA+10|110右形0010(A←A+Nt0001/0(在形0100:CC764=101160neoe. cetl Iec,10h-Ilool onto K=-T二u100l.例求解11初恒为0永截神0M+,形考A人初,oo管AA-A401nom号在6o011彩·将1001011I44+9101。106o0右移20101019101011右形「。。10100在补位臂号n移,n形卫得点运臂X= XM x2X2MEEX+y=(Xm X2x2ESE十YAXE-TEXx二(XD)x2变(=)x-Y=(Am2XE-TEx/YXA/以人点加腐以而墓原理).对操限操7教4置对D下密差,小硝同大砖看,所强小是事同在移但,每市,可1)教两加对里碳上,以对能行相应)将笼果格吧:如录足都不是规格形龙,则各信果满得点数而乘豫法两午将点极刘来,支形的育为两乘所丽违形,其尾赦为两来数尾数勇个将南相阶商的网丽被徐超两成种国商的尾为减毅勇尾除以乘敬的尾搬检加料队同说是长度相词函两进制烯面对应格不司的比奇偶验吨用奇烛验时,校的设置变候证特报得和成中的的截力都使甲成验要原的限5出=骂:如运算CkC城酸乘除善除以中如派席换武减例M)=1D0.G=101求莫循序兄余校的解0(n3,Mn)=10000/1100600·2=-100060DGU)01000余R)=010·可借那兄余为0ABD·x2+20=10010=(0D Tu)传是T6,W硝正一侵梯位的汉码的位的港取频合2-2m+k4m为倍夏色的都,极验应汉丽C排放在殇亠这十9+98十14+414+1Hu HnH1。?H0DDe Ds C4 D4 D3D C D, CC1=H1二Hbs①H7①日?凸2二A=2份H6田H7Jpo③HC2=H4二HH6出1⑤4=H2=H701.nBC4C2C1Bc4c3c,=0→误0果十进剧→误色置
- 2020-12-01下载
- 积分：1
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稀疏自编码深度学习的Matlab实现
稀疏自编码深度学习的Matlab实现，sparse Auto coding，Matlab codetrain, m/7% CS294A/CS294W Programming Assignment Starter CodeInstructions%%%This file contains code that helps you get started ontheprogramming assignment. You will need to complete thecode in sampleIMAgEsml sparseAutoencoder Cost m and computeNumericalGradientml For the purpose of completing the assignment, you domot need tochange the code in this filecurer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencodtrain.m∥%%========%6% STEP 0: Here we provide the relevant parameters valuesthat willl allow your sparse autoencoder to get good filters; youdo not need to9 change the parameters belowvisibleSize =8*8; number of input unitshiddensize 25number of hidden unitssparsity Param =0.01; desired average activation ofthe hidden units7 (This was denoted by the greek alpharho, which looks like a lower-case pcurer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencod4/57train.,m∥in the lecture notes)1 ambda=0.0001%o weight decay parameterbeta 3%o weight of sparsity penalty term%%==:79 STEP 1: Implement sampleIMAGESAfter implementing sampleIMAGES, the display_networkcommand shouldfo display a random sample of 200 patches from the datasetpatches sampleIMAgES;display_network(patches(:, randi(size(patches, 2), 204, 1)), 8)%为产生一个204维的列向量,每一维的值为0~10000curer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencod5/57train.m/v%中的随机数,说明是随机取204个 patch来显示%o Obtain random parameters thetatheta= initializeParameters ( hiddenSize, visibleSize)%%=============三三三三====================================97 STEP 2: Implement sparseAutoencoder CostYou can implement all of the components (squared errorcost, weight decay termsparsity penalty) in the cost function at once, butit may be easier to do%o it step-by-step and run gradient checking (see STEP3 after each stepWecurer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencod6/57train. m vb suggest implementing the sparseAutoencoder Cost functionusing the following steps(a) Implement forward propagation in your neural networland implement the%squared error term of the cost function. Implementbackpropagation tocompute the derivatives. Then (using lambda=beta=(run gradient Checking%to verify that the calculations corresponding tothe squared error costterm are correctcurer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencod7/57train. m vl(b) Add in the weight decay term (in both the cost funcand the derivativecalculations), then re-run Gradient Checking toverify correctnessl (c) Add in the sparsity penalty term, then re-run gradiChecking toverify correctnessFeel free to change the training settings when debuggingyour%o code. (For example, reducing the training set sizecurer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencod8/57train m vl/number of hidden units may make your code run fasterand setting betaand/or lambda to zero may be helpful for debuggingHowever, in yourfinal submission of the visualized weights, please useparameters web gave in Step 0 abovecoS七grad]sparseAutoencoderCost(theta, visibleSize,hiddensize, lambda,sparsityParam, beta,patches)二〓二二二二二二二〓二〓二〓二〓=二====〓=curer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencod9/57train.m vlll96% STeP 3: Gradient CheckingHint: If you are debugging your code, performing gradienchecking on smaller modelsand smaller training sets (e. g, using only 10 trainingexamples and 1-2 hiddenunits) may speed things upl First, lets make sure your numerical gradient computationis correct for a%o simple function. After you have implemented computeNumerun the followingcheckNumericalGradientocurer:YiBinYUyuyibintony@163.com,WuYiUniversityDeep Learning, MATLAB Code for Sparse Autoencode10/57
- 2020-12-05下载
- 积分：1
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