基于Matlab的语音合成技术

elsevier word 投稿模板, 方便不熟悉ctex的朋友

这是一个用javaee开发的图书管理系统，实现的技术是SSH和jdbc，servlet的一点技术，功能还是比较全面的。

快递分拣机器人设计文档（不含源码）

其中SC2262和SC2272的两份完整中文资料，内容齐全，还包括一份振荡电阻的匹配选择，和应用电路图，适合初学者学习。

车牌识别matlab实现 车牌定位、字符分割、字符识别是车牌识别的三个重要组成部分,是车牌识别技术研究的重点。

pdf文档，中文来源：华北电力大学学报242007[11997199811.2199910.88408200.68000.47800.2掩76072007000100120时间/天68040100120图3训练过程中的误差变化时间/天Fig.3 Errors during training图1日负荷预测时洲练数据4Fig. 1 Training data for day load for ecas ting35←只51.5050-10203040如0的070809010101200.8时间压图4训练过程中神经元个数变化Fig 4 Grow th of neurons during train ing0.200.95系0.940.8505101520253035滞后时间/天如0.80.75图2训练数据的自相关系数Fig. 2 Autocorrelation coefficients of training dat a(a拟合曲线0.〔(3)10.040.0系70/14060[1000000]时间/天(b拟合误差「O1000001图5训练结果及误差Fig. 5 Result and error of train in3- EBF6,C1994-2012cHinaAcademicJournalElectronicPublishingHouse.Allrightsreservedhttp://www.cnki.net4EUNITE2582080078076074072026.26700(3)66005101520253035时间/天50/1[00001图6日最大负荷预测结果[01010]26ig. 6 Result of forFrecasting g1 SOFNN[716.2,739.7,757.7,781.3],7Tab 1 Accuracy of training and forecast ing[720.1,738.2,763.7,MethodMAPE/ (%O) ME767.7],7SOFNN1.3344.137901.7850.04780EUNITE1.95E770760winner750报告中ME值没有准确堤供,但可以从报告中估轵740计得出约50~60完整的 EUNITE网络竟赛原始数据可730从EUNITE网站获得(http://neuron.tuke.sk/compe-720tit ion/ index. php)7103354EUNITEMAPE时间1.95[2]图7周平均最大负荷预测结果3.2Fig. 7 Forecasting result of week average max loadSOFNNSOFNNWSj72SOF NNWLWLideltaWli, delta WLi=WLj-Wli(3h:× delt al+k× deltal wj,Yh=0.58o1994-2012ChinaAcademicJournalElectronicPublishingHouse.Allrightsreservedhttp:/www.cnki.net262007820[1] Chen B J, Chang M W, Lin C J. Load forecasting using800support vector machines: a study on eunitE com-780tion 2001[J]. IEEE transactions on power systems7602004.19(4):1821-1830[2] Company behind East- Slov akia Power Distrib ution740Com panWorle w ide com petition w ithin the EU720NITE network, EUNITE competiton report [RI700[3]6802004.28(17):1-1105101520253035[4 Leng g, Prasad g, McGinnity T M. An on line algor-时间/天rithm for creating self organ izing fuzzy neural networ ks图8修正后的日最大负荷预测结果Neural Netw orks, 2004,(17): 1477-1493[5 Ort iz: Arroyo D, Skov M K, Huynh Q. Accurate Elee-F ig.8 Forecasting results after rev sedtricity Load Forecasting with Artificial Neur al NetworksIC. Proceedings of the 2005 International Conference2SOFNNon Compu tat io nal Intel ligence for M odel ling, Control andTah 2 Accuracy of forecast ingAuto mation. and International Conference on I ntelligentMethodMAPE/(%)MEAgents, Web Technolo gies and Internet CommerceSOFNN1.7850.04(CIMCAIA WTIC 05). 20051.5941.95[ 6] Tao X. Input dimens ion reduction for load forecastingEUNITE1.9550-60based on support vector machines [C]. Hong Kong82004 IEEE International Conference of Electric U til yderegulation, res tructur ing and pow er technolog iesMAPE ME20[7 Hsu CC. Dynam icall y Optim izing Parameters in SupportVector Regression An A pp licat io n of Electricity Load4结论Forecasting [C]. Haw aii: Proceedings of the 3 9thIlaw aii International Conference on System Sciences199918 Pan kratz A. Forecasting w ith Univariate Box-JenkinsModels [m. John Wiley sons, 1983SOFNN(1972-),,C1994-2012cHinaAcademicJournalElectronicPublishingHouse.Allrightsreservedhttp://www.cnki.net

【实例简介】该文档为3GPP国际标准组织发布的最新5G规范，R16，主要为基站无线发送和接收技术方面，主要章节为： 1 Scope 2 Reference 3 Definitions,symbols and abbreviations 4 General 5 Operating bands and channel arrangement 6 Conducted transmitter characteristics 7 Conducted receiver characteristics 8 Conducted performance requirements 9 Radiated transmitter characteristics 10 Radiated receiver characteristics 11 Radiated performance requirements Annex A (normative): Reference measurement channels Annex B (normative): Error Vector Magnitude (FR1) Annex C (normative): Error Vector Magnitude (FR2) Annex D (normative): Characteristics of the interfering signals Annex E: Void Annex F (normative): Relationship between EIRP based regulatory requirements and 3GPP requirements Annex G (Normative): Propagation conditions Annex H (informative): Change history

关于指纹识别的两个完整实例代码，一个是国外的，另外一个是FVC国际竞赛实测的，此资源对于学习相关知识有比较大的帮助

[代码及相关实验]---代码为C语言编写1、CPU看门狗实验2、LED跑马灯实验3、CPU Timer定时器实验4、实时时钟实验5、AD转换实验6、扩展SDRAM读写实验7、扩展FLASH读写实验8、快速傅立叶变换(FFT)实验9、FIR滤波器实验10、IIR滤波器实验11、自适应滤波器（FIRLMS）实验12、键盘扫描实验13、外部中断输入实验14、AIC23播音实验15、LCD显示实验16、串口通信实验17、USB2.0通信实验18、网络通信实验19、SD卡读写实验20、MMC卡读写实验21、数字图像直方图实验22、数字图像边缘检测实验23、数

动态矩阵DMC算法 matlab程序实现