Rational rose license.upd 注册码 注册文件

成绩管理程序 小键盘按键识别数码管移位显示 微机串口通信功能综合检测 打字计时练习 定时器计数器 PWM脉宽调制等共计30个源程序

DSP CCS的入门、进阶的经典合集。内含cmd文件的具体编写，以及烧写flash的相应步骤等等。

张正友相机标定Opencv实现实例源码张正友相机标定Opencv实现实例源码张正友相机标定Opencv实现实例源码张正友相机标定Opencv实现实例源码张正友相机标定Opencv实现实例源码

Vivado Licence 永久使用版 适用版本：适用于Vivado的任何版本. 643个永久IP license. 11个2022年超期HLS license。1.打开Vivado License Manager（注册文件管理器） 2.点击Load License 3.将几个license文件逐一导入 4.在View License Status查看license适用版本与过期日期

VVVF控制方法 电机控制，详细讲解了VVVF的推导和电流环的控制◆若忽略定子漏阻抗的影响,则有,U1=4.44f Wkwl若想保持平不变,则应有U1/f1=常数(32)这就是F协调控制的原理,在VVF变频器广泛采用控制方法特点:1这是由静态模型得到的,因此它不强调动态性能;2这是在忽略定子漏阻抗的影响得到的,在频率比较低时,这种忽略会带来偏小,电机力矩不够,一般要进行补偿3.一般的ⅴ/控制如图3.1所示。额定频率以下采用恒力矩调速,额定频率以上采用恒功率调速。V/F曲线不同的负载应采用不同的V/F控制曲线odin. com图3.1V/F控制曲线3.2PWM遊变器的模型VVVF变频器一般采用电压源型逆变器(VSI),并采用PWM控制方法,图3.2是PWM逆变器和异步电机的等效框图。+U图3.2PWM逆变器模型开关函数在逆变器中定义开关函数SU、Sv、Sw为:T通T4截止时Su=1,反之Su=0;T3通T截止时Sv=1,反之Sv=0T2通T截止时Sw=1,反之Sw=0相电压在定义了开关信号之后,很容易得到△DM=m2n=o+△OM=1n+rm+6n+△Oh1u Svo t VON11v+LR,+vσdtWNLwo V-Iiw+e+ vON由于逆变器三相输出无中线,n+14+1=0将以上三式相加,得ON=(SU+sy+sw)uee当三相逆变器的反电势之和err t er t eW0即三相反电势平衡时,AO1A=3(2n+24+2)nq于是可得到逆变器电机模型为「din1(Su +Sy +sw)Rd iUUR(S + sU+sw)Wd iLWRWW(S+S、+Sw)WWdC0L dt(33)3.3规则采样的SPWM方法331规则采样SPWM的生成自然采样法和规则采样法是生成SPWM的两种主要方法自然采样法适合用模拟电路完成。而规则采样法适合用微计算机数字实现。在当今数字化时代,规则采样被广泛采用

在特征波长选取方面有一些创新，可以作为参考。在特征波长选取方面有一些创新，可以作为参考。（基于高光谱成像的蓝莓内部品质检测特征波长选择方法研究古文君1 ，田有文 1* ，张芳1 ，赖兴涛 1 ，何宽1 ，姚萍1 ，刘博林 2）586-482016620010~15mm0.8~2.3g。fone3:(InSpector V10E, Spectral InFinland)1392pix×1040pixCCDL CCD2(IGV-B141OM, IMPERX Incorporated, USA), 150W1. CCD Camera; 2.Spectrometer; 3.Shot; 4. Light source; 5. Samples(3900 Illuminatior, Illumination Tech6.Translationplatform7.Lightsourcecontroller;8.computernologies inc.,USA)、(IRCP0076-19. Translation platform controllerCOM,)、(120cm×50cmx(DELL VoStro 5560D-1528Figure 1 Schematic diagram of hyperspectral imagingcmsystem400~1000nm,4722.8nmRRGY-4(10mm)(DBR45(successive projections algorithm, SPA(stepwise multiple linear regression, SMLR)(SPA)(SMLR)SPASPASMLRSPA-SPA、SMLR_SMLR、SPA- SMLRSMLR-SPA21994-2018ChinaAcadcmicJournalElcctronicPublishingHousc.Allrightsrcscrved.http://www.cnki.nct5871.6BP(error back propagation)BP17(correlation coeffiient of calibration, Re)(root mean square error of calibration set, RMSEC)correlation coeffiient of pre-diction, Rp)(root mean square error of prediction set, RMSEP)ENVI 4.8(Research System Inc, ), MATLAB 2014a(The Math Works Inc)、TheUnscrambler9.7、 Excel2010(Ⅵ icrosoftdgle banddWcvef.BP models for soluble solidsThe selected characteristic wavelengthCurve of relative reflectanceExtract the region of interescontent and firmness prediction2figure 2 Flow chart of data processing280mm,68ms,28mm·s-。99%202.2600nm600nm2b2c)21994-2018ChinaAcadcmicJournalElcctronicPublishingHousc.Allrightsrcscrved.http://www.cnki.nct5884823(2f)BPSavitzky-Golasavitzky -golayTable 1 The effect of different spectra preprocessingCalibration setPredictioSpectrum typeRMSECRMSEPOriginal spcctrum0.933/0.9230.3510.4040.9200.9100.508/0.319MSCThe spectrum after MSC processing0.940/0.9450.56lO.3120.9190.9320.516/0.282SNThe spectrum after SNV processin0.93709340.60210.24309220.9010.6320.462Savitzky-golayThe spectrum after Savitzky-Golay processing 0.955/0.9550.3240.2410.951/0.9490.400/0.2782.5SPA-SPA SMLRSMLR SPA-SMLR SMLR-SPASPA-SPASPASavitzky-GolaySPATable 2 The results of multi-stage characteristic wavelength selection methodnmCharacteristie wavelength selection methodSPA-SPA452,455,470,482,490,785,893,912,921,942,950455,470,482,785,893.912SMLR-SMLR457,508,516,534,543,51,556,568,712,720.774,778508,534,543,712,720,774SPA-SMLR452,455,470,482,490,785,893,912,921,942,950452,470,482,490,893,912SMLR-SPA457,508,516,534,543,551,556,568,712,720,774,78534,7202.6Savilzky-gola(FS)392SPA-SPASMLR-SMLRSMLR-SMLRSMLR-SPABPBP0.001500021994-2018ChinaAcadcmicJournalElcctronicPublishingHousc.Allrightsrcscrved.http://www.cnki.nct589BPBPSPA-SPARp RMseP0.9520.391°Brix,RpRMSEP0.9530.234BrixTable 3 Detection results of soluble solid content and firmness of blueberry based on different multi-stagecharacteristic wavelength selection methodsCalibration setPrediction setCharacteristic selection method Wavelength numberRMSECRMSEP3929550.9550.324/0.2410.9510.9490.400/0.278SPA-SPA0.9590.9560.3180.1530.9520.9530.391/0.234SMLR-SMLR0.9560.9340.414/0.243912109020.559/0.349SPA SMLR0.828/0.8581.3670.58582208091.440/0.719SMLR- SPA20.958/0.9360.402/0.3359320.9280.435/0,4041387nm1229nm91.5%BPRRMSEP0.904215.163lBP3Rv0.84V0.94Rv0.83,SEV0.63。400-1000nmSavitzky-GolayBPSPA-SPASPA-SPA21994-2018ChinaAcadcmicJournalElcctronicPublishingHousc.Allrightsrcscrved.http://www.cnki.nct59048[1 KADER F,ROVEL. B Fractionation and identification of the phenolic compounds of highbush blueberries(Vaccinium corymbosumLUJ].Food Chemistry, 1996,55(1): 35-40「J,2012,33(1):340-342,2017,38(2):301-305.[4 MENDOZA F, LU R, ARIANA D,et al. Integrated spectral and image analysis of hyperspectral scattering data for prediction ofple [ruil firmness and soluble solids conlenl[J] Poslharvesl Biology and Technology, 2011, 62(2: 149-160[5 SUN M J, ZHANG D, LIU L,et al. How to predict the sugariness and hardness of melons a near-infrared [J]. Food Chemistry,2017,218(3:413-42116 SIEDLISKA A, BARANOWSKI P, MAZUREK W, ct al. Classification models of bruise and cultivar detection on the basis of hy-perspectral imaging data[J]. Computers and Electronics in Agriculture, 2014, 106: 66-74[7 LIU D, SUN D W, ZENG X N, el al. Recenl aDvances in wavelength seleclion lechniques for hyperspectral image processing inthe food industry[J]. Food Bioprocess Technol, 2014, 7: 307-323[8 ZHANG C, GUO C T, LIU F,et al. Hyperspectral imaging analysis for ripeness evaluation of strawberry with support vector ma-chine[j] Journal of Food Engincering, 2016, 179: 11-18[9J,2016,47(5:634-6402009,29(:1611-1615201536(12)171-17612]J,2012,32(11:3093309[13] LI B C, HOU B L, ZHANG D W,et al. Pears characteristics (soluble solids content and firmness prediction, varieties) testingInethods based on visible-near infrared hyperspecTral imaging[J]. OpLik, 2016, 127: 2624-2630[14] FAN S X, ZHANG B H,LI J B, et al. Prediction of soluble solids content of apple using the combination of spectra and textural features of hyperspectral reflectance imaging data[J. Postharvest Biology and Technology, 2016, 121: 51-61[15 RAJKUMAR P, WANG N,EIMASRY G, et al.Studies on banana fruit quality and maturity stages using hyperspectral imaging[ JIJournal of Food Engineering 2012, 108: 194-200,2015,36(16):10172015,35(8:2297-2302[18]WANG N,2007,23(2:151-155.「192008,39(5):91-9320」201536(10:70-74.[21] WU D, SUN D WAdvanced applications of hyperspectral imaging technology for food quality and safety analysis and assess-ment a review part T[J]. Innovative Food Science and Emerging Technologies, 2013, 19(4): 1-14J2014,35(8:57-61BP,2012.124」13,44(2):142-146.25],201523(6:1530-1537M011:41-48.[27,2013,24(10:1972-19762010,30(10):2729-2733?1994-2018ChinaAcadcmicJournaleLcctronicPublishingHousc.Allrightsreservedhttp://www.cnki.nct

由STM32作为主控，RFID采集信息，AT24C02保存信息，OLED进行显示，矩阵键盘输入密码等模块组成可管理员控制增、减用户，用户可刷卡开门的智能门禁系统。

这是一个VC++编写的飞机仪表显示的例子，适合与初学者

手把手教你学DSP--基于TMS320C55x光盘 含电路图+代码陈泰红 出 版 社：北京航空航天大学出版社 本书(作者陈泰红、任胜杰、魏宇)以TMS320C55x系列高性能低功耗DSP为主，主要介绍了以数字信号处理器(DSP)为核心的实时数字信号处理器的硬件结构和片内外设，论述了 eXpress算法标准软件尤其是CCS的使用，详细说明了DSP与外围接口电路的设计以及最小系统的设计，给出了DSP相关软件编程和开发调试，还介绍了 MATLAB在数字信号处理中的应用和DSP／BIOS基础知识。在介绍功能模块的基础上，列出了相应的实战项目开发实例，并讲述了DSP+FPGA复杂系统的设

混合高斯背景建模，MATLAB，用的是背景差分和实时背景更新