同济大学VB.net 课件（402页）

【实例简介】XILINX Spartan6 XC6SLX9 FPGA开发板ALTIUM设计硬件原理图PCB+AD集成封装库，4层板设计，大小为130x90mm，Altium Designer 设计的工程文件，包括完整的原理图及PCB文件，可以用Altium(AD)软件打开或修改，已制样板测试验证，可作为你产品设计的参考。集成封器件型号列表： Library Component Count : 44 Name Description ---------------------------------------------------------------------------------------------------- 1117-3.3 24LC04B_0 4148 CAP NP_Dup2 CP2102_0 C_Dup1 C_Dup2 C_Dup3 C_Dup4 Circuit Breaker Circuit Breaker Connector 15 Receptacle Assembly, 15-Pin, Sim Line Connector DS1302_8SO EC Fuse 2 Fuse HEX6 HY57651620/SO_0 Header 2 Header, 2-Pin Header 7X2 Header, 7-Pin, Dual row Header 9X2 Header, 9-Pin, Dual row INDUCTOR KEYB LED LED_Dup1 M25P16-VMN3PB 16 Mb (x1) Automotive Serial NOR Flash Memory, 75 MHz, 2.7 to 3.6 V, 8-pin SO8 Narrow (MN), Tube MHDR2X20 Header, 20-Pin, Dual row MiniUSBB OSC PNP R RESISTOR RN RN_Dup1 R_Dup1 R_Dup2 R_Dup3 R_Dup5 R_Dup6 SD SPEAKER SRV05-4 SW KEY-DPDT XC6SLX9-2FTG256C Spartan-6 LX 1.2V FPGA, 186 User I/Os, 256-Ball Fine-Pitch Thin BGA (1.0mm Pitch), Speed Grade 2, Commercial Grade, Pb-Free ZTA battery

【实例简介】xilinx zynq7000开发流程，设计到ISE和linux配置方面的内容。

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

51单片机ADC0832电压测量液晶1602显示的C程序与proteus仿真，所有资料。

诸位，这个文件是我从淘宝花了五十多买下来的，绝对好使。 功能是一种简易信号发生器（频率（从1-100hz）、幅度可调（5v之内）），四种波形输出（正弦波 三角波 方波 锯齿波），附带proteus仿真以及c文件。 然后程序都有代码的注释，需要改频率调节的可以直接去改就行，都好使呢。 拿这个做毕设，做论文，写仿真，都是可以的呢。

【实例简介】适合目标检测，yolov3,ssd等目标检测算法，数据集包含xml坐标信息的标签。pytorch版本下的yolov3训练实现火焰检测(https://codingchaozhang.blog.csdn.net/article/details/107167792#comments_14120984)

【实例简介】pytorch docs 保存于网页，完美pdf版，完整公式、图片、表格，有页码，有目录，有书签导航，适用电脑、pad、手机上浏览。 === Printed v0.4.1 from [https://pytorch.org/docs/stable/ ] at 2018-10-10 15:17:41. Visit [ https://download.csdn.net/user/ldengjie/uploads ] to get the latest version pdf or mail to ldengjie@163.com to ask for printing and updating the latest version pdf. === 《pytorch tutorials v1.0.0.dev20181002 官方文档PDF版》 在 https://download.csdn.net/download/ldengjie/10712480 ，保存时间 2018-10-10 21:03:44 ===

完整版，详细，前端学习的宝典.内容包括HTML,CSS,JAVASCRIPT的内容，讲解详细易懂，掌握其中的原理

Boost+LLC闭环调节器设计，mathcad详细的计算步骤，推荐使用mathcad14.0打开

【实例简介】SWMM5.1.014版代码基础上进行一定扩展，可以用来进行swmm模拟以及out、inp文件的内容读取的教学案例