As a laser passes through a scattering medium, the light interacts with the irregular reflections within the medium, resulting in light scattering and the formation of speckles. In this paper, an image sensor based on the combination of a coreless optical fiber and a digital camera is proposed for liquid refractive index sensing applications. The coreless fiber is used as a sensing unit, and the change in the speckle pattern is measured using the digital correlation method to detect the magnitude of the liquid’s refractive index. The experimental results indicate that the laser image sensing technique is capable of effectively distinguishing liquid samples with refractive indices ranging from 1.332 8 to 1.390 8, with a sensing sensitivity of −1.306 RIU-1. Moreover, the laser image sensing technique, with its advantages of high experimental reproducibility, simple system design, remote over-control, holds great research significance and potential application in laser communication and sensor integration.

In this paper, we have calculated the structural, electronic, and optical properties of chalcogenide stannite Cu2CdSnX4 (X=S, Se, Te) materials. The calculations are based on the density functional theory (DFT) method and are performed using the Cambridge sequential total energy package (CASTEP) code included in the Biovia Material Studio 20 software. All optical properties have been studied in a domain that extends energetically from 10 meV to 40 eV. Our results show that Cu2CdSnX4 (X=S, Se, Te) stannite exhibits absorption in the visible region, the refractive index decreases with increasing energy, and the refractive index values are n=3.2, 3.73 and 3.75 for Cu2CdSnS4, Cu2CdSnSe4 and Cu2CdSnTe4, respectively. They show also high conductivity, which implies that this material is promising for solar cells. These results argue in favor of the use of these materials in various potential applications. The density of state, band structures, and structural properties of Cu2CdSnX4 (X=S, Se, and Te) stannite are also studied in this work.

Wireless ultraviolet (UV) has strong scattering characteristics and can communicate through non-direct vision. When UV signals are transmitted in the atmosphere, they are affected by the absorption and scattering effects of atmospheric particles and atmospheric turbulence, resulting in attenuation of UV signal energy and reduced reliability of the communication system. This paper focuses on the channel model of UV non-direct-view single scattering communication, and simulates and analyzes the communication characteristics of UV light in atmospheric turbulence and mixed aerosol environment under horizontal, vertical and oblique range communication scenarios. The results show that at equal relative humidity, the wireless UV non-directive scattering communication performance for vertical communication scenarios is more affected by the mixed aerosol environment and the communication performance is worse.

In this paper, the performance of a two-way relay (TWR) mixed radio frequency/free space optical (RF/FSO) system with co-channel interference (CCI) is investigated. Different from interference-limited one-way relay (OWR) system, CCIs should be considered at both relay and users in TWR system. Additionally, opportunistic user schedule is applied to select the user with the largest signal to interference plus noise ratio (SINR). Exact and asymptotic outage probability (OP) is derived. Approximate bit error rate (BER) is further presented to evaluate the overall performance. Simulation results show that the performance of a TWR system is dominated by the uplink or downlink whose performance is severer.

The detection of small targets poses a significant challenge for infrared search and tracking (IRST) systems, as they must strike a delicate balance between accuracy and speed. In this paper, we propose a detection algorithm based on spatial attention density peaks searching (SADPS) and an adaptive window selection scheme. First, the difference-of-Gaussians (DoG) filter is introduced for preprocessing raw infrared images. Second, the image is processed by SADPS. Third, an adaptive window selection scheme is applied to obtain window templates for the target scale size. Then, the small target feature is used to enhance the target and suppress the background. Finally, the true targets are segmented through a threshold. The experimental results show that compared with the seven state-of-the-art small targets detection baseline algorithms, the proposed method not only has better detection accuracy, but also has reasonable time consumption.

This paper proposes a lightweight reinforcement network (LRN) and auxiliary label distribution learning (ALDL) based robust facial expression recognition (FER) method. Our designed representation reinforcement (RR) network mainly comprises two modules, i.e., the RR module and the auxiliary label space construction (ALSC) module. The RR module highlights key feature messaging nodes in feature maps, and ALSC allows multiple labels with different intensities to be linked to one expression. Therefore, LRN has a more robust feature extraction capability when model parameters are greatly reduced, and ALDL is proposed to contribute to the training effect of LRN in the condition of ambiguous training data. We tested our method on FER-Plus and RAF-DB datasets, and the experiment demonstrates the feasibility of our method in practice during rehabilitation robots.

To address the challenges of varied aircraft skin hole detection types and susceptibility to noise, this paper proposes a method based on the perspective of dual contour edge information fusion. The core method combines stereoscopic vision and structured light dual contour information consistently, focusing on extracting edge point information around the hole edge to achieve precise detection of circular holes. In this approach, a line multi-directional gradient feature detector (LMGFD) is introduced for locating the holes from plane stereoscopic image. Furthermore, we establish a three-dimensional (3D) circular hole detection method (BPCircle) based on the dual contour edge information fusion. Finally, experiments demonstrate that our proposed method achieves superior accuracy and robustness based on public benchmark dataset and our own collected standard IPCDS dataset (including two-dimensional (2D) images, 3D point clouds, and measured data of three-coordinate measuring machine). The dataset and code can be found from https://github.com/Nicho1sdqw/123.

Drone photography is an essential building block of intelligent transportation, enabling wide-ranging monitoring, precise positioning, and rapid transmission. However, the high computational cost of transformer-based methods in object detection tasks hinders real-time result transmission in drone target detection applications. Therefore, we propose mask adaptive transformer (MAT) tailored for such scenarios. Specifically, we introduce a structure that supports collaborative token sparsification in support windows, enhancing fault tolerance and reducing computational overhead. This structure comprises two modules: a binary mask strategy and adaptive window self-attention (A-WSA). The binary mask strategy focuses on significant objects in various complex scenes. The A-WSA mechanism is employed to self-attend for balance performance and computational cost to select objects and isolate all contextual leakage. Extensive experiments on the challenging CarPK and VisDrone datasets demonstrate the effectiveness and superiority of the proposed method. Specifically, it achieves a mean average precision (mAP@0.5) improvement of 1.25% over car detector based on you only look once version 5 (CD-YOLOv5) on the CarPK dataset and a 3.75% average precision (AP@0.5) improvement over cascaded zoom-in detector (CZ Det) on the VisDrone dataset.

The autocollimator is an important device for achieving precise, small-angle, non-contact measurements. It primarily obtains angular parameters of a plane target mirror indirectly by detecting the position of the imaging spot. There is limited report on the core algorithmic techniques in current commercial products and recent scientific research. This paper addresses the performance requirements of coordinate reading accuracy and operational speed in autocollimator image positioning. It proposes a cross-image center recognition scheme based on the Hough transform and another based on Zernike moments and the least squares method. Through experimental evaluation of the accuracy and speed of both schemes, the optimal image recognition scheme balancing measurement accuracy and speed for the autocollimator is determined. Among these, the center recognition method based on Zernike moments and the least squares method offers higher measurement accuracy and stability, while the Hough transform-based method provides faster measurement speed.