Wavelength stability in a hybrid photonic crystal laser through controlled nonlinear absorptive heating in the reflector

The need for miniaturized,fully integrated semiconductor lasers has stimulated significant research efforts into realizing unconventional configurations that can meet the performance requirements of a large spectrum of applications,ranging from communication systems to sensing.We demonstrate a hybrid,silicon photonicscompatible photonic crystal(PhC)laser architecture that can be used to implement cost-effective,high-capacity light sources,with high side-mode suppression ratio and milliwatt output output powers.The emitted wavelength is set and controlled by a silicon PhC cavity-based reflective filter with the gain provided by a Ⅲ–Ⅴ-based reflective semiconductor optical amplifier(RSOA).The high power density in the laser cavity results in a significant enhancement of the nonlinear absorption in silicon in the high Q-factor PhC resonator.The heat generated in this manner creates a tuning effect in the wavelength-selective element,which can be used to offset external temperature fluctuations without the use of active cooling.Our approach is fully compatible with existing fabrication and integration technologies,providing a practical route to integrated lasing in wavelength-sensitive schemes.

Deep learning of DEM image texture for landform classification in the Shandong area,China

Landforms are an important element of natural geographical environment,and textures are the research basis for the spatial differentiation,evolution features,and analysis rules of the landform.Using the regional difference of texture to describe the spatial distribution pattern of macro landform features is helpful to the landform classification and identification.Digital elevation model(DEM)image texture,which gives full expression to texture difference,is key data source to reflect the surface features and landform classification.Following the texture analysis,landform features analysis is assistant to different landforms classification,even in landform boundary.With the increasing accuracy requirement of landform information acquisition in geomorphic thematic mapping,hierarchical landform classification has become the focus and difficulty in research.Recently,the pattern recognition represented by Convolutional Neural Network has made great achievements in landform research,whose multichannel feature fusion structure satisfies the network structure of different landform classification.In this paper,DEM image texture was taken as the data source,and gray level co-occurrence matrix was applied to extract texture measures.Owing to the similarity of similar landform and the difference of different landform in a certain scale,a comprehensive texture factor reflecting landform features was proposed,and the spatial distribution pattern of landform features was systematically analyzed.On this basis,the coupling relationship between texture and landform type was explored.Thus,the deep learning method of Convolutional Neural Network is used to train the texture features,and the second-class landform classification is carried out through softmax.The classification results in small relief and mid-relief low mountains,overall accuracy are 84.35%and 69.95%respectively,while kappa coefficient are 0.72 and 0.40 respectively,were compared to that of traditional unsupervised landform classification results,and the superiority of Convolutional Neural Network classification was verified,it approximately improved 6%in overall accuracy and 0.4 in kappa coefficient.