daleloogn的专栏

mfc 界面插件，包含office各类界面源码，亲测vs2010 x64 x86可用。vs2017 不稳定。

工作需要在vs2010 64为开发环境下使用水印，在网上找到了libharu。优势：免费又可以做水印。 文档说明： libharu\libhpdf为64位编译dll的项目，libharu\libhpdf\x64路径下的debug和release为对应的64为下编译的dll文件。 已经使用验证，很好使用。

要做二维码的读写，在网上找了很多资源，都不能直接测试使用，整合了问题和处理，整理一下发布。二维码使用zbar库，解决其中乱码问题。使用vs2010作为编译环境，把zbar编译dll真个项目打包，把测试二维码生成和识别例程打包。以x64为例。

ReadMe Release Version beta_1.0 index.py imageMatlab.py This is more or less a wrapper for Matplotlib imaging functions such that their behavior is equivalent, in terms of colormap, aspect and so forth, to the expected behavior of Matlab's functions. sepVocal.py This script can be used to execute the desired separation. See below for an example of use of this file. SIMM.py This script implements the actual algorithm for parameter estimation. It is mainly used by sepVocal.py. tracking.py The Viterbi decoding algorithm is implemented in this script. Requirements: These scripts have been tested with Python 2.7, The packages that are required to run the scripts are pydub,ffmepg, Numpy, Spicy, Matplotlib. One can respectively find the latest versions at the following addresses: http://pydub.com/ https://ffmpeg.org http://numpy.org/ http://scipy.org/ http://matplotlib.sourceforge.net/ Notes: Prefer recent versions of the above packages, in order to avoid compatibility issues, notably for Matplotlib. Note that this latter package is not necessary for the program to run, although you might want to watch a bit what is happening! Spicy should be version 0.8+, since we use its io.wavefile module to read the wave files. We once used the audio lab module, but it would seem that it is a bit more complicated to install (with the benefit that many more file formats are allowed). Usage: The easy way to use these scripts is to run the exec package of our release version: http://www.github.com/beata_1.0 for more develop: you can run the index.py on pycharm directly. note: the output files will create under you source wav file. ContactMe Email:[email protected]

Auto Speaker Recognition main.py the main file for test audio_record.py record audio from micro phone count_days.py count days between two date. 20110805 20160903 mfcc_feature.py extract mfcc feature from wav files SGD.model* the trained model on train set , and the accurate is 70% util.py contains the most useful functions train train data is 75% of all the data test test data is 25% of all the data and has no overlap with train set classification_SGD.py is the main classification function py file , and it used the sklearn's SGD niter was set 10000 could get 70% of accurate. classification_BNB.py this is the sklern naive_bayes BernoulliNB , and it reach to just 56% classification_DT.py this is the sklern tree.DecisionTreeClassifier , and it reach to just 63% classification_GB.py this is the sklern GradientBoostingClassifier, and it reach to the best now of 76% when n_estimators=1000, but it produce too many model components to store. classification_GNB.py this is the sklern naive_bayes GaussianNB, and it reach to just 63% vote_result.py add a vote decsion , every method have the acurrcy number ticiks to vote the final answer. after the vote , we achived 96% at test set. beta1.0

August 26, 2015 IEEEtran is a LaTeX class for authors of the Institute of Electrical and Electronics Engineers (IEEE) transactions journals and conferences.

This is a small library that can train Restricted Boltzmann Machines, and also Deep Belief Networks of stacked RBM's. Train RBM's: %train an RBM with binary visible units and 500 binary hidden model= rbmBB(data, 500); %visualize the learned weights visualize(model.W); Do classification: model= rbmFit(data, 500, labels); prediction= rbmPredict(model, testdata); Train a Deep Belief Network with 500,500,2000 architecture for classification: models= dbnFit(data, [500 500 2000], labels); prediction= dbnPredict(models, testdata); see included example code for more I can be contacted on andrej.karpathy@ gmail. NOTE: This was a class project that I worked on for 1 month and then abandoned development for almost 4 years ago. Please do not send me specific questions about issues with the code or questions on how to do something. I only put this code online in hope that it can be useful to others but cannot fully support it. If you would like pointers to more actively maintained implementations, have a look here (https://github.com/rasmusbergpalm/DeepLearnToolbox) or maybe here (https://github.com/lisa-lab/DeepLearningTutorials) Sorry and best of luck! 原文：http://code.google.com/p/matrbm/

Code provided by Ruslan Salakhutdinov and Geoff Hinton Permission is granted for anyone to copy, use, modify, or distribute this program and accompanying programs and documents for any purpose, provided this copyright notice is retained and prominently displayed, along with a note saying that the original programs are available from our web page. The programs and documents are distributed without any warranty, express or implied. As the programs were written for research purposes only, they have not been tested to the degree that would be advisable in any important application. All use of these programs is entirely at the user's own risk. How to make it work: 1. Create a separate directory and download all these files into the same directory 2. Download from http://yann.lecun.com/exdb/mnist the following 4 files: o train-images-idx3-ubyte.gz o train-labels-idx1-ubyte.gz o t10k-images-idx3-ubyte.gz o t10k-labels-idx1-ubyte.gz 3. Unzip these 4 files by executing: o gunzip train-images-idx3-ubyte.gz o gunzip train-labels-idx1-ubyte.gz o gunzip t10k-images-idx3-ubyte.gz o gunzip t10k-labels-idx1-ubyte.gz If unzipping with WinZip, make sure the file names have not been changed by Winzip. 4. Download Conjugate Gradient code minimize.m 5. Download Autoencoder_Code.tar which contains 13 files OR download each of the following 13 files separately for training an autoencoder and a classification model: o mnistdeepauto.m Main file for training deep autoencoder o mnistclassify.m Main file for training classification model o converter.m Converts raw MNIST digits into matlab format o rbm.m Training RBM with binary hidden and binary visible units o rbmhidlinear.m Training RBM with Gaussian hidden and binary visible units o backprop.m Backpropagation for fine-tuning an autoencoder o backpropclassify.m Backpropagation for classification using "encoder" network o CG_MNIST.m Conjugate Gradient optimization for fine-tuning an autoencoder o CG_CLASSIFY_INIT.m Co

ccf认定的会议分级，内容为2012版。复旦大学认可。

气动机械手PLC程序设计,毕业论文设计开发，可供参考，有需要的下载看看。

摘　要 随着全球信息化的不断深入，移动通信的客户群已长大成为一个增长的趋势。在通信市场竞争的日益激烈，用户需求和网络质量越来越高的业务需求的绝大部分，如何提高网络运行性能，提高网络服务质量，已成为移动通信企业掌握主动权和加强核心竞争力的基本前提。因此，深化对移动网络优化的重要性日益增加，这都是为了发展业务也以一种积极的促进作用。本文的研究目标是投入运行的网络进行数据采集，数据分析，确定采用技术手段或者网络的参数以达到最好的运行状态调整网络运行质量的影响因素，为优化效率，网络资源。在同一时间，提高用户的感知的基础上，更注重于解决业务问题端网络和提高网络设置的合理性，提高了网络资源利用率，消除网络的问题，降低了网络的运营风险，提高网络以适应新技术和新业务未来的能力。 目前有更多或更少的优化软件的缺点，在实际应用过程中，所以迫切改善工作遇到的问题，从提高工作效率，优化工作流程的角度来看，这篇文章的主题是基于移动Airone WLAN网络优化分析软件，它不仅包括传统的无线网络的性能优良的工具，也注重不断变化的业务优化的发展导致的需求变化，通过创新的特点，对网络的质量和性能提供了有效的保护。此外，该平台还提供多标准，多终端的测试能力，在图像和数据业务实现的优化的最大值，并保证了测试效率，有效利用的稳定性，准确性和易用性。 文章首先介绍了移动通信网络的基本知识，主要的工作内容网络优化分析及相关的技术要点，解析后的基于网络的设计思想Airone优化软件的重点，实施方法和关键技术，最后对软件的具体优化项目的操作使用，取得了良好的效果，体现了优化软件的设计与开发的实际价值。 关 键 词：无线局域网；AP；网络优化；WLAN；AirOne

超宽带（UWB）系统能够传输的数据在很宽的频带的频谱具有非常低功耗和高数据速率的一种新的无线技术。在可能的应用中，UWB技术可以被用于成像系统中，车辆与地面穿透雷达和通讯系统。特别是，它是一个用来在家里或在办公室用，具有每秒数百个数据兆位的无线连接取代几乎所有的电缆。虽然超宽带标准（IEEE802.15.3a标准）尚未完全确定，大部分建议的应用程序被允许发送一个乐队3.1-10.6GHz之间的。两种可能的方法都出现了利用分配的频谱。一个是多频带的方法，以14500MHz的子频带，OFDM调制，可能的话，一个跳频方案。另一种可能性是所谓的“脉冲无线电”的基础上，非常短的脉冲的传输，有脉冲位置或极性调制。在许多方面，从现有的无线技术和标准，作为调制方式，多址接入技术，以及发射器/接收器结构的UWB好处是适于超宽带。 本文讨论了IEEE802.1 WLAN接收机前端LNA的设计，分别采用CMOS工艺和HEMT过程中的低噪声放大器的研究与设计的2.4GHz的工作。TM的CMOS低噪声放大器的设计流片和测试。首先，从802.1WLAN系统中的索引，接收器在无线局域网中使用的系统参数计算过程的详细分析，比较和分析各个接收机质量的结构。第三部分介绍了低噪声放大器的设计，分析了几种基本结构的放大器的优点和参数，采用小信号模型和噪声模型，分析CMOS低噪声放大器的增益，线性度，噪声系数的一个详细的分析性能参数MOSFET的各种噪声分量，并反馈于LNA估算MOSFET的类型，大小和由上述参数得到寄生参数的性能的感应源的负效应，电路设计和低噪声放大器的布局设计的估算公式进行了分析。其中，考虑到噪声级联管引入的噪声估计，极串联电感反馈结构噪声的计算方法与来源，结构优化设计。 关 键 词：低噪声放大器;UWB;电路

摘　要 供配电CAD技术是计算机技术在电气工程设计领域的另一应用。但是我国供配电CAD技术仍停留在绘图和计算相分离的阶段，还未形成一体化的CAD系统，而供配电系统的设计是一项复杂的工程，它包括需求分析、各级负荷容量的确定、高低压配电系统设计、变电所设计等，需要分析计算和绘制大量的图纸，工作量庞大。 本文针对上述情况，分析研究了6kV及以下低压供配电系统的设计原则和设计内容，查找设计资料、设计规程规范、各种相关电气设计手册，了解设计需要的各种数据，并在研究了电气图形、供配电方案、数据间内在联系机理及其关系的基础上，对供配电系统进行拓扑描述和结构识别；建立了6kV及以下低压供配电一次部分的变压器、线路、断路器、电压电流互感器、熔断器、开关等主要设备元件对象的描述方法和关联知识规则；采用面向对象技术和图示化技术，设计了表达电气设备特征的基本图元库、支路图元库及模板图形库；创建了实现分析计算、绘图、数据处理一体化的供配电一次CAD系统的总体结构框架，完成了负荷计算与无功补偿、变压器选型、主结线确定、电气设备选型等各功能模块的设计；并能同时进行图形输出和各种文档的打印输出。 本系统基于windows操作平台，采用可视化编程语言Visual Basic和通用绘图软件AutoCAD作为开发工具，运用专家系统支持的智能化技术、ActiveX二次开发AutoCAD技术和数据库技术，实现了低压供配电设计、数据处理和绘图一体化。并在最后以一个机械厂变配电所的设计实例表明了系统设计的正确性和有效性。本文对6Kv及以下低压供配电CAD系统研究的目的是为了更好的提高工作效率，促进低压供配电CAD技术的发展，使配电设计技术能向更高层次的、集成化的、智能化的和标准化的方向发展，是电气设计工作人员工作的发展方向。 关 键 词：变电所；低压配电；自动化系统

%Demonstrates the mcrvm algorithm on a toy example (Three class classification problem). Part of the code was %derived from Tipping's matlab code. % @file mcrvm_example.m % Author Arasanathan Thayananthan ( [email protected]) % (c) Copyright University of Cambridge % % This library is free software; you can redistribute it and/or % modify it under the terms of the GNU Lesser General Public % License as published by the Free Software Foundation; either % version 2 of the License, or (at your option) any later version. % % This library is distributed in the hope that it will be useful, % but WITHOUT ANY WARRANTY; without even the implied warranty of % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU % Lesser General Public License for more details. % % You should have received a copy of the GNU Lesser General Public % License along with this library; if not, write to the Free Software % Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA

很使用的一款参考文献管理工具，在你需要的时候随处插入，自动按顺序引入并在插入位置编号。关联性很强，易于使用，在写论文时极佳。

中文latex套件ctex使用宝典教程

一个很小巧的工具 帮助你测试读写硬盘或者u盘的速度

基于角色访问控制管理模型的安全性分析，基于角色访问控制管理模型的安全性分析。

这个是华为的面试题 比较全面 多年累积的 内部传出来的 可以下载看看。希望对你有用。

很经典的SQL宝典，内部资料。请鉴赏

js权威指南，一本书 pdf的 大家可以下载看看

基于Java的聊天室设计与实现，附带论文，开题

wine已测试好用！linux下安装exe文件。这个版本比较新了，最重要的是测试过很好使！分享给大家需要的拿去！