Linux-Oracle11g最全rpm包（64位）

ALIENTEK精英STM32开发板作者：唯恋殊雨 CSDN博客：https://blog.csdn.net/tichimi337512864_SPI_OLED_STM32驱动 OLED显示驱动模块：SSD1306 四线SPI:SCL,SDA 对应接口：PC2,PC1 实验器材: 正点原子精英STM32F103开发板  实验目的: 学习单色OLED显示屏的使用 硬件资源: 1,DS0(连接在PB5) 2,ALIENTEK 0.96寸OLED模块(连接IO说明,见oled.h文件)  实验现象: 本实验利用开发板的OLED/CAMERA接口连接ALIENTEK OLED模块，实现OLED模块的显示，下 载成功后，通过把OLED模块插入开发板左下角的OLED/CAMERA模块接口，按下复位之后，就 可以看到OLED模块不停的显示ASCII码和码值。 注意事项: 1,开发板的OLED/CAMERA模块接口比OLED的插针多2个,OLED靠左插即可. 2,例程默认用的8080方式驱动OLED模块,所以OLED模块的BS0,BS1都需要接VCC(默认接的VCC).

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

设有最大化的整数规划问题A,与它对应的线性规划为问题B,从解问题B开始,若其最优解不符合A 的整数条件,那么B的最优目标函数必是A 的最优目标函数 的上界,记作Z1;而A 的任意可行解的目标函数值将是 一个下界Z2。分支定界法就是将B的可行域分成子区域(称为分支),逐步减小Z1和增大Z2,最终求到 .

雷蛇幻彩版键盘灯光工程文件超过100种文件,使键盘更体验效果

用Verilog写的浮点除法器，作为初学者的参考文件！

【实例简介】用MATLAB编写的三维重建程序，包括角点检测，匹配和重建，但是貌似有点小问题。

【实例简介】磁通观测说明： ///////////////////////////0519主要程序 flux_d=flux_d_o+T*(edss+20*reg3*cos_theta-20*flux_d_o); flux_q=flux_q_o+T*(eqss+20*reg3*sin_theta-20*flux_q_o); ///////////////磁通的大小在0.3-0.8之间 if(flux=0.8) reg3=0.8; end if(flux=1)) reg3=abs(we); if(we>=0) flux_d_comp=flux_d+flux_q; flux_q_comp=flux_q-flux_d; else flux_d_comp=flux_d-flux_q; flux_q_comp=flux_d+flux_q; end % else % reg3=1; % flux_d_comp=flux_d; % flux_q_comp=flux_q; % end %----------------------- flux_d_r=(Lr/Lm)*(flux_d_comp-(o*Ls*idss)); flux_q_r=(Lr/Lm)*(flux_q_comp-(o*Ls*iqss)); ///////////////////////////////// ////////////////////////////在磁通观测的这些已有的m文件中， 0254，0617，0623，0701采用的方法是zw论文中提及的p34-p35的方法， 其中函数之间的区别是在参数上作了调整。0518，0519方法同上。 0520采用不同方法？？

用逐点添加的方法对散点建立delaunay三角网，适合教学用

如何让导航仪进入wince系统 1、使用“ToWinCe.exe”，就可以进入Win CE桌面，方法是在“导航路径设置”中将路径指向“ToWinCe.exe”即可。

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