Aiming at the problem of low positioning accuracy of laser SLAM algorithm in indoor scenes with feature scarcity and narrow corners, a laser inertial SLAM method based on planar extension and constraint optimization is proposed. The IMU is fused in laser SLAM, and the laser point cloud is position compensated and key frames are judged according to the IMU state estimation results. The global planar map is constructed, the planar extraction of key frames is performed based on the RANSAC algorithm and combined with the pre-extraction method to track the planar features in order to reduce the time cost, and the fitting results are optimized by iPCA to remove the effect of noise on the RANSAC. Using the distance from the point to the surface to construct the plane constraint optimization equation, and integrate it with the edge point constraints and pre-integration constraints in a unified way to establish a nonlinear optimization model, and solve to get the optimized plane information and key frame bit position. Finally, to verify the effectiveness of the algorithm, experiments are carried out on the M2DGR public dataset and private dataset respectively, and the experimental results show that the present algorithm performs well on most of the public datasets, especially in the private dataset compared with the widely used fast-lio algorithm, the localization accuracy is improved by 61.9%, which demonstrates good robustness and real-time performance.

The main light sources used in the clinical treatment of urological surgery are thulium-doped laser, holmium-doped laser, and green laser via the double-frequency from neodymium-doped laser, etc. In recent years, with the improvement of the output power of blue semiconductor laser diodes, 450 nm blue light has attracted growing attention and been applied in bladder tumor resection surgery, offering advantages such as clean cutting, minimal bleeding, and no adverse coagulation of adjacent tissues. This work focuses on the solution for a high-stability fiber-coupled output blue laser source for urological surgery applications. A 350 W fiber-coupled blue semiconductor laser is built by utilizing four 100 W arrayed blue laser units as the light source. The optical field transmission characteristics of the multi-emitter array are analyzed, and the far-field distribution of optical intensity exhibits a dual-peak structure with a peak angle of arcsin(5λ/4γd). By applying the spatial beam combining technique, we have successfully achieved the cross-interference of the slow-axis beams, thereby obliterating the emission dead zone. A polarization beam combining scheme is performed to rotate the polarization state of one beam from P-polarized to S-polarized, and then combine it orthogonally with another P-polarized beam, resulting in compression of the spacing between fast-axis beams and improved beam brightness. The collimating structure reduces the divergence angles of the fast and slow axes to 0.6981 mrad and 1.0123 mrad, respectively. The fast axis is expanded by a factor of 1.2 to transform the output beam profile into a square shape. Finally, we obtain a blue laser with a power of 358 W, an output fiber of 200 μm/NA 0.22, a beam combining efficiency of 89.5%, an electro-optical conversion efficiency of 31.3%, and power fluctuation less than 0.4%. Using the water-cooled fiber to couple out the light beam, this high-power laser source may serve as an ideal medical solution for clinical treatment in urological surgery.

A real-time image recording system was designed to record spot wander formed by two orthogonal laser beams, and a series of laser beam atmospheric transmission field experiments were conducted to compare the spot wander and study atmospheric turbulence’s anisotropy by inversion. The experimental results show that the atmospheric turbulence near the ground is anisotropic which can be manifested as follows: 1) It is related to the wind direction. A significant difference between vertical wander and horizontal was recorded when the angle between the transmission path and the wind direction was small. When the laser transmission path is orthogonal to the wind direction, vertical wander is basically the same as the horizontal. 2) It is related to real-time temperature. Among the four wander components of two orthogonal laser beams, an uneven distribution of spot wander was found, and temperature decrease increases the intensity of the uneven distribution. Based on the comprehensive analysis of research results, this paper proposes a concept named atmospheric turbulence anisotropy intensity index A which can quantify characteristics and intensity of atmospheric turbulence’s anisotropy.

Sun glint is a significant confounding factor in passive optical remote sensing images. To mitigate this issue, a polarizer is typically incorporated in front of the remote sensor, leveraging the linear polarization characteristics of sun glint. The suppression effects depend on the relative position of the sun and the remote sensor, as well as the directions of polarizers. In this paper, we introduce a novel onboard system for the real-time computation of Sun glint polarization parameters, devised specifically for a spaceborne atmospheric correction instrument. Utilizing three channel polarization images (at 0°, 60°, and 120°) in the 670 band of the spaceborne atmospheric correction, we calculate the sun glint parameters and compared them against the 6S radiation transfer model, excluding image pixels heavily influenced by the could. The system is implemented using the V5 series Field Programmable Gate Array (FPGA) as the hardware platform, and the High-Level Synthesis Tool (HLS) as the software platform. The performance of the system is verified through a simple laboratory experiment, which demonstrates a calculation deviation within 0.5°. In terms of computational efficiency, the system processes a 25x25 pixel dataset in 19.47281 ms using a 100 MHz clock, with the highest resource utilization rate reaching 41%.

Whole slide imaging (WSI) is the main basis for cancer diagnosis and prognosis, characterized by its large size, complex spatial relationships, and diverse styles. Due to its lack of detailed annotations, traditional computational pathology methods are difficult to handle WSI tasks. To address these challenges, this paper proposes a WSI survival prediction model based on graph neural networks, BC-GraphSurv. Specifically, we use transfer learning pre-training to extract features containing spatial relationship information and construct the pathological relationship topology of WSI. Then, the two branch structures of the improved graph attention network (GAT) and graph convolution network (GCN) are used to predict the extracted features. We combine edge attributes and global perception modules in GAT, while the GCN branch is used to supplement local details, which can achieve adaptability to WSI style differences and effectively utilize topological structures to handle spatial relationships and distinguish subtle pathological environments. Experimental results on the TCGA-BRCA dataset demonstrate BC-GraphSurv's effectiveness, achieving a C-index of 0.795—a significant improvement of 0.0409 compared to current state-of-the-art survival prediction models. This underscores its robust efficacy in addressing WSI challenges in cancer diagnosis and prognosis.

An FPGA-based field programmable gate array (FPGA) and wavelength-modulated tunable diode laser absorption spectroscopy (WM-TDLAS) technique have been combined to develop a programmable gate array WM-TDLAS CO2 concentration detection system. Leveraging the programmable nature of FPGA chips, a digital lock-in amplifier (DLIA) with signal acquisition and modulation, as well as harmonic demodulation functions, was designed to meet the application requirements. To validate its performance, harmonic extraction tests, Q-factor assessments, and anti-noise experiments were conducted. The results revealed a linearity of 99.99% for the target frequency extraction and a Q-factor of up to 45. In the harmonic extraction experiments for signals with different signal-to-noise ratios, the maximum relative error in the mean value was only 0.91% when the signal-to-noise ratio was 43 dB. Using a distributed feedback laser with a center wavelength of 1572 nm as the light source, covering the absorption line at 6360 cm?1, and utilizing an effective optical path of 14 m in a dense multi-pass gas absorption cell, gas concentration detection experiments were carried out. The system demonstrated a fitting linearity of 99.982% between the detected concentration and the amplitude of the second harmonic. By increasing the scanning wavelength frequency, the system was capable of obtaining concentration values in 0.1 seconds. The Allan variance results showed that the detection limit of the system was 1.86 ppm when the integration time was 44 seconds. The experimental results indicate that the developed WM-TDLAS detection system based on an FPGA array features high detection accuracy, rapid response, strong stability, and miniaturization, making it suitable for real-time concentration monitoring in practical applications.

Due to the different reflective properties of the diffuse and specular components in composite surface objects and the limitations imposed by the camera depth of field, defocusing of sinusoidal fringes occurs in specular imaging, leading to phase errors. To achieve the efficient and high-precision measurement of composite surface objects, this paper proposes a method for three-dimensional surface topography measurement by combining defocused binary patterns with sinusoidal fringes. Firstly, the paper partitions and calibrates the defocus level of the system based on the edge and second-order blur methods, addressing the issue of varying defocus levels of the reference surface due to the tilted placement of the camera. Then, a binary fringe phase error model is established to determine the optimal fringe width and the defocus range. Finally, defocus compensation is applied to the binary fringes in the slightly defocused region, ensuring that the captured fringes are within the optimal defocus range. Three-dimensional surface topography measurement is conducted based on this approach. Experimental results show that the proposed method reduces the error in the specular component from 0.033 mm to 0.019 mm, thereby improving the accuracy of composite surface measurement.

Detection of floating debris in rivers is of great significance for ship autopilot and river cleaning, but the existing methods in targeting floating objects in the river with small target sizes and mutual occlusion, and less feature information lead to low detection accuracy. To address these problems, this paper proposes a small target object detection method called PAW-YOLOv7 based on YOLOv7. Firstly, in order to improve the feature expression ability of the small target network model, a small target object detection layer is constructed, and the self-attention and convolution hybrid module (ACmix) is integrated and applied to the newly constructed small target detection layer. Secondly, in order to reduce the interference of the complex background, the Omni-dimensional dynamic convolution (ODConv) is used instead of the convolution module in the neck, so as to give the network the ability to capture the global contextual information. Finally, the PConv (partial convolution) module is integrated into the backbone network to replace part of the standard convolution, while the WIoU (Wise-IoU) loss function is used to replace the CIoU. It achieves the reduction of network model computation, improves the network detection speed, and increases the focusing ability on the low-quality anchor frames, accelerating the convergence speed of the model. The experimental results show that the detection accuracy of the PAW-YOLOv7 algorithm on the FloW-Img dataset improved by the data extension technique in this paper reaches 89.7%, which is 9.8% higher than that of the original YOLOv7, the detection speed reaches 54 frames per second (FPS), and the detection accuracy on the self-built sparse floater dataset improves by 3.7% compared with that of YOLOv7. It is capable of detecting the tiny floating objects in the river channel quickly and accurately, and also has a better real-time detection performance.