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.

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.

The echo signal of light detection and ranging (LiDAR) system is easily disturbed by various noises under the influence of intense background light. The signal to noise ratio (SNR) decreases with increasing distance, which seriously affects the retrieval accuracy of the LiDAR system. This paper combines local mean decomposition and an improved thresholding method (LMD-ITM) to process the Lidar signal with a lot of noise, so as to avoid the loss of useful information. In this research, the correlation coefficient and energy entropy are used to distinguish between the relevant and irrelevant components decomposed by the local mean decomposition. Subsequently, the improved thresholding method is applied to process the irrelevant components. It can extract the effective signals which are difficult to be separated from the high-frequency signals. Finally, all components are reconstructed to get the denoising signal. The experimental results demonstrate that this method can effectively avoid the short-range migration, suppress the long-range noise, and adapt to different weather conditions. Compared with the previous denoising method, the proposed method increases the SNR by about 15%.

Wafer defect detection and classification are essential for ensuring the quality of semiconductor wafers, optimizing production efficiency. However, existing methods often fail to process shallow and deep feature information concurrently, restricting their capacity to utilize multi-level features for accurate classification. To overcome this limitation, this paper introduces a novel dual-path architecture, DPFEE-Net, which integrates PeleeNet’s dense connection structure and multi-channel feature fusion techniques with the deep feature extraction capabilities of Convolutional Neural Networks (CNNs). By combining these two approaches, DPFEE-Net effectively captures both shallow and deep features, enhancing the detection of critical wafer surface defect patterns. Additionally, squeeze-and-excitation (SE) attention mechanism is incorporated, enabling the model to prioritize defect-prone areas in images, further improving classification accuracy. Experimental results demonstrate that DPFEE-Net achieves a remarkable average accuracy of 96.8% on the WM-811K dataset, surpassing existing methods such as WM-PeleeNet, WDD-SCA and MobileNetV2. Moreover, the model delivers superior detection performance with reduced computational complexity and parameter requirements, making it highly suitable for practical deployment in production environments.

An actively mode-locked (ML) fiber laser is demonstrated to produce a 198 picosecond (ps) short-pulse train, featuring voltage-control repetition rates. A cost-effective strategy for overcoming the weak light absorption in graphene is demonstrated by integrating graphene into etched single-mode fiber (SMF), with advantages of adequate light-graphene interaction and integration of fiber system. The active modulation is realized by a graphene fiber modulator (GFM) in the ring fiber cavity, which shows a transparency modulation of 3.4 dB. As a result, stable active fundamental and second-order harmonic mode locking are demonstrated, allowing the generation of stable pulse trains with repetition rates of 12.4 and 24.7 MHz around 1558.3 nm. Such fiber device based on graphene can be feasible seeds for optical fiber light source in many applications.