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我也要
收获
1.整理了google reader的订阅，现在在google reader上花的时间太多了。
参考http://www.williamlong.info/archives/2172.html
2.修改host来使用dropbox，这样方法不是长久之计。
3.搭了个GAppProxy方便在其他地方看网页。
4.eclipse -clean是万能的 。
遇到的问题
1.使用spring的AOP，然后发现拿到的bean通过反射获得的成员和方法是代理类的。没有找到一个合理的方法来过滤这些成员和方法。在这里使用AOP确实有点不合适，但又想不出更好的方法。
2.在heritrix中模拟iphone的浏览器失败了。
Idea
1.暑假的时候实现Android上的卡卡颂游戏。
rsyslog交叉编译（armv7、armv8、aarch64、arm32平台通用）
该文档描述了如何交叉编译rsyslog到arm64嵌入式平台。
QTP11.5(HP UFT 11.5)下载地址
QTP11.5(HP UFT 11.5)下载地址http://automationqa.com/forum.php?mod=viewthread&tid=1380&fromuid=29
SCHUNK SVH五指灵巧手 | 熵洛智能
雄克仿真五指机械手产品系列可以如人手操作那般完美地完成抓取操作。电子装置完全集成于腕关节中，使五指机械手几乎可以完成所有的人体手部动作。凭借带有9个驱动器的运动指骨，机械手可高精度地 执行多种抓取操作。富有弹性的抓取表面确保了对物体的可靠抓取。除了开启抓取和操作任务的全新领域之外，雄克还为在五指机械手的手势基础上进行人与机器人的交流开创了无限可能。编辑切换为居中添加图片注释，不超过 140 字（可选）产品特点：机械手有左手或右手两种版本适合移动应用耗能低，使用 24 V DC 在腕关节处完整集成了控制、调节
rdkx5部署yolov11
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Lumerical官方案例、FDTD时域有限差分法仿真学习（三）——环形谐振器(Ring resonator)之第二＆第三部分
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告别外置电阻！揭秘MAX22201集成电流检测的5大设计优势（附PCB布局案例）
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flash Android 11.5,增强稳定性 Flash Player 11.5已发布
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时序定江山，功耗写风骨——解读时序库lib文件的奥义【芯片设计 lib文件】
lib库，全称liberty library format file：它是Liberty时序文件的简称，使用 Liberty 语法来编写。它可以通过library compiler/dc转换成.db文件(database)，供synopsis家的DC和PT使用，也可以通过spyglass的library compiler来生成sglib文件，供spyglass使用。他们是等价的，但db文件是二进制格式，人类不可读。
YOLO5和YOLO11的区别
YOLO5和YOLO11的区别
ARINC429协议分析
如下表所示，ARINC429在发送一帧过程中，首先发送以START开始，以END结束，共发送32bits数据，其中0~7bits为Lable数据，9~8bits为SDI数据，10~28bits为数据，29~30bits为SSM，31bit为校验位End30292827262524校验位SSM2322212019181716DATA15141312111098SDI7654321Start。
【C++】开源：cpp-httplib HTTP协议库配置与使用
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QTP11 5 HP UFT 11 5 下载地址
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Debian 11安装及配置fcitx5
Debian 11官方版本，默认输入法有3种，分别为fcitx,fcitx5,xim.从DVD盘安装Debian 11 Xfce时，默认无法调用输入法。但如果从Debian 11 Xfce Live盘安装Delian 11 Xfce时，输入法是已安装好了的，可直接调用。下面谈谈从DVD盘安装Debian 11 Xfce时，如何安装fcitx5输入法。 第1步 打
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深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/)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")
Statistics and Machine Learning Toolbox
Statistics and Machine Learning Toolbox（带书签），MATLAB统计学习工具
CentOS 5下安装Oracle 11g
Oracle公司宣称在Linux下安装Oracle9i数据库至少要有512MB的内存和至少1GB或者两倍 内存大小的交换空间，对于系统内存大于2GB的服务器，交换空间可以介于2GB—4GB之间。 如果是为了在一台仅有256M内存的普通PC机上试用Oracle9i，在分配了1GB左右的交换空间的情况下，也可以正常运行Oracle数 据库。
Data-Structures-and-Algorithm-Analysis-in-Java.pdf
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pb11.5所用到的dll
pb8升级到pb11.5 pb11.5编译的程序发布
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