Output Prediction of Helical Microfiber Temperature Sensors in Cycling Measurement by Deep Learning

The inconsistent response curve of delicate micro/nanofiber(MNF)sensors during cycling measurement is one of the main factors which greatly limit their practical application.In this paper,we proposed a temperature sensor based on the copper rod-supported helical microfiber(HMF).The HMF sensors exhibited different light intensity-temperature response relationships in single-cycle measurements.Two neural networks,the deep belief network(DBN)and the backpropagation neural network(BPNN),were employed respectively to predict the temperature of the HMF sensor in different sensing processes.The input variables of the network were the sensor geometric parameters(the microfiber diameter,wrapped length,coiled turns,and helical angle)and the output optical intensity under different working processes.The root mean square error(RMSE)and Pearson correlation coefficient(R)were used to evaluate the predictive ability of the networks.The DBN with two restricted Boltzmann machines(RBMs)provided the best temperature prediction results(RMSE and R of the heating process are 0.9705℃and 0.9969,while the values of RMSE and R of the cooling process are 0.7866℃and 0.9977,respectively).The prediction results obtained by the optimal BPNN(five hidden layers,10 neurons in each layer,RMSE=1.1266℃,R=0.9957)were slightly inferior to those obtained by the DBN.The neural network could accurately and reliably predict the response of the HMF sensor in cycling operation,which provided the possibility for the flexible application of the complex MNF sensor in a wide sensing range.

Recognizing the orbital angular momentum(OAM)of vortex beams from speckle patterns

The orbital angular momentum(OAM)of vortex beams offers a new degree for information encoding,which has been applied to optical communications.OAM measurement is essential for these applications,and has been realized in free space by several methods.However,these methods are inapplicable to estimate the OAM of vortex beams directly from the speckle patterns in the exit end of a multimode fiber(MMF).To tackle this issue,we design a convolutional neural network(CNN)to realize 100%accuracy recognition of two orthogonally polarized OAM modes from speckle patterns.Moreover,we demonstrate that even when the speckle patterns are cropped to only 1/64 of the original patterns,the recognition accuracy of the designed neural network is still higher than 98%.We also study the recognition accuracy of cropped speckles in different areas of speckle patterns to verify the feasibility of OAM recognition after cropping.The results demonstrate that recognizing the OAMs of two orthogonally polarized vortex beams from only a portion of speckle patterns in the exit end of an MMF is feasible,offering the potential to construct a 1×N data transmission scheme.

Propagation characteristics of a high-power broadband laser beam passing through a nonlinear optical medium with defects

The intensity distributions of a high-power broadband laser beam passing through a nonlinear optical medium with defects and then propagating in free space are investigated based on the general nonlinear Schr¨odinger equation and the split-step Fourier numerical method. The influences of the bandwidth of the laser beam, the thickness of the medium,and the defects on the light intensity distribution are revealed. We find that the nonlinear optical effect can be suppressed and that the uniformity of the beam can be improved for a high-power broadband laser beam with appropriate wide bandwidth. It is also found that, under the same incident light intensity, a thicker medium will lead to a stronger self-focusing intensity, and that the influence of defects in the optical elements on the intensity is stronger for a narrowband beam than for a broadband beam.