Although lidar is a powerful active remote sensing technology, lidar echo signals are easily contaminated by noise, particularly in strong background light, which severely affects the retrieval accuracy and the effective detection range of the lidar system. In this study, a coupled variational mode decomposition (VMD) and whale optimization algorithm (WOA) for noise reduction in lidar signals is proposed and demonstrated completely. The combination of optimal VMD parameters of decomposition mode number K and quadratic penalty α was obtained by using the WOA and was critical in acquiring satisfactory analysis results for VMD denoising technology. Then, the Bhattacharyya distance was applied to identify the relevant modes, which were reconstructed to achieve noise filtering. Simulation results show that the performance of the proposed VMD-WOA method is superior to that of wavelet transform, empirical mode decomposition, and its variations. Experimentally, this method was successfully used to filter a lidar echo signal. The signal-to-noise ratio of the denoised signal was increased to 23.92 dB, and the detection range was extended from 6 to 10 km.

Reliable estimation of the atmospheric boundary layer height (ABLH) is critical for a range of meteorological applications, including air quality assessment and weather forecasting. Several algorithms have been proposed to detect ABLH from aerosol LiDAR backscatter data. However, most of these focus on cloud-free conditions or use other ancillary instruments due to strong interference from clouds or residual layer aerosols. In this paper, a machine learning method named the Mahalanobis transform K-near-means (MKnm) algorithm is first proposed to derive ABLH under complex atmospheric conditions using only LiDAR-based instruments. It was applied to the micro pulse LiDAR data obtained at the Southern Great Plains site of the Atmospheric Radiation Measurement (ARM) program. The diurnal cycles of ABLH from cloudy weather were detected by using the gradient method (GM), wavelet covariance transform method (WM), K-means, and MKnm. Meanwhile, the ABLH obtained by these four methods under cloud or residual layer conditions based on micropulse LiDAR data were compared with the reference height retrieved from radiosonde data. The results show that MKnm was good at tracking the diurnal variation of ABLH, and the ABLHs obtained by it have remarkable correlation coefficients and smaller mean absolute error and mean deviation with the radiosonde-derived ABLHs than those measured by other three methods. We conclude that MKnm is a promising algorithm to estimate ABLH under cloud or residual layer conditions.

Although recent breakthroughs in reported graphene-based phototransistors with embedded quantum dots (QDs) have definitely been astonishing, there are still some obstacles in their practical use with regard to their electrical and optical performances. We show that through optimization of the vertical graphene nanoribbon (GNR)/QD/IGZO heterostructure and the ultrahigh efficiency of CsPbBr₃ QDs, it is possible to significantly increase the on/off ratio (>10³), the subthreshold slope (S.S., 0.9 V dec⁻¹), the device's field effect mobility (μ_FET, 13 cm⁻¹ V⁻¹ S⁻¹) and other electrical properties. Subsequently, on the basis of the extra optical–electrical characterization, we attribute the enhanced photosensitivity (800), the accelerated detecting speed (141 μs) and the high detectivity (7.5 × 10¹⁴ cm Hz^1/2 W⁻¹) to the vertical heterostructure associated with the optimized GNR component. To further demonstrate this enhancement phenomenon, the mechanism and theory mode of this vertical heterostructure are analyzed and exploited in this letter. This research indicates that a highly sensitive and fast phototransistor can be realized using the novel GNR/QD/IGZO vertical heterostructure and the long diffusion length of the perovskite QD photosensing component.

In this paper, we propose a novel ultrafast laser with nanostructure CH ₃ NH ₃ PbI ₃ nanorods, yielding a higher modulation depth and lower optical loss. Our experiment is performed through nanopatterned CH ₃ NH ₃ PbI ₃ rods and an all-solid-state passive mode-locked laser system. The prepared nanoarray CH ₃ NH ₃ PbI ₃ perovskite exhibits a modulation depth of 8.1% under a saturation intensity of 9.53 MW/cm ² . By applying the CH ₃ NH ₃ PbI ₃ nanoarray as a saturable absorber in the ultrafast 1064-nm laser system, a mode-locked pulse with a 30-ps pulse width and an 87.3-MHz repetition rate is achieved. A maximum output power of 542 mW was measured, under an absorption pump power of 5.4 W. With the new structured CH ₃ NH ₃ PbI ₃ , our experiment verifies a new method in ultrafast lasers with low light loss, which potentially uncovers an avenue to develop ultrafast lasers.

The signal-to-noise ratio of lidar signals decreases rapidly with an increase in distance, which seriously affects the application of lidar detection technology. Variational mode decomposition (VMD) has performed optimality in dealing with noise, but the number of modes, $K$ , and the penalty parameter, $\alpha $ , must be preset. Therefore, a novel lidar signal denoising method that combines VMD with machine learning online optimization (MLOO) and the interval thresholding (IT) technique, named VMD-MLOO-IT, is proposed in this article. The proposed method defines new fitness functions to evaluate the result of VMD-based denoising, and selects the optimal parameters by the model which development by MLOO. In addition, IT is used to deal with the recovered signal. The experimental results demonstrate the superiority of the presented method over the other empirical mode decomposition-based and VMD-based denoising methods.