New ultrashort pulsewidth measurement technology based on interference jitter and FPGA platform

Conventional ultrashort pulsewidth measurement technology is autocorrelation based on second-harmonic generation;however,nonlinear crystals and bulky components are required,which usually leads to the limited wavelength range and the difficult adjustment with free-space light alignment.Here,we proposed a compact all-fiber pulsewidth measurement technology based on the interference jitter(IJ)and field-programmable gate array(FPGA)platform,without requiring a nonlinear optical device(e.g.nonlinear crystal/detector).Such a technology shows a wide measurement waveband from 1 to 2.15μm at least,a pulsewidth range from femtoseconds to 100 ps,and a small relative error of 0.15%-3.8%.In particular,a minimum pulse energy of 219 fj is experimentally detected with an average-power-peak-power product of 1.065×10^(-6)W^(2).The IJ-FPGA technology may offer a new route for miniaturized,user-friendly,and broadband pulsewidth measurement.

Measuring loblolly pine crowns with drone imagery through deep learning

In modeling forest stand growth and yield,crown width,a measure for stand density,is among the parameters that allows for estimating stand timber volumes.However,accurately measuring tree crown size in the field,in particular for mature trees,is challenging.This study demonstrated a novel method of applying machine learning algorithms to aerial imagery acquired by an unmanned aerial vehicle(UAV)to identify tree crowns and their widths in two loblolly pine plantations in eastern Texas,USA.An ortho mosaic image derived from UAV-captured aerial photos was acquired for each plantation(a young stand before canopy closure,a mature stand with a closed canopy).For each site,the images were split into two subsets:one for training and one for validation purposes.Three widely used object detection methods in deep learning,the Faster region-based convolutional neural network(Faster R-CNN),You Only Look Once version 3(YOLOv3),and single shot detection(SSD),were applied to the training data,respectively.Each was used to train the model for performing crown recognition and crown extraction.Each model output was evaluated using an independent test data set.All three models were successful in detecting tree crowns with an accuracy greater than 93%,except the Faster R-CNN model that failed on the mature site.On the young site,the SSD model performed the best for crown extraction with a coefficient of determination(R^(2))of 0.92,followed by Faster R-CNN(0.88)and YOLOv3(0.62).As to the mature site,the SSD model achieved a R^(2)as high as 0.94,follow by YOLOv3(0.69).These deep leaning algorithms,in particular the SSD model,proved to be successfully in identifying tree crowns and estimating crown widths with satisfactory accuracy.For the purpose of forest inventory on loblolly pine plantations,using UAV-captured imagery paired with the SSD object detention application is a cost-effective alternative to traditional ground measurement.