There is limited amount of research on surface plasmon resonance (SPR) sensors with self-referencing capabilities which are based on dielectric gratings. In the short-wavelength range, a metal grating sensor is capable of simultaneously measuring liquid refractive index under proposed temperature. A fabricated gold grating is placed on one side of a thin gold film for refractive index measurement, while the other with polydimethylsiloxane (PDMS) is deposited on the other side for temperature measurement. We use finite element analysis to research its sensing characteristics. Due to the high refractive index sensitivity of SPR sensors and thermo-optic coefficient of PDMS, we discovered the maximum spectral sensitivity of the sensor is 564 nm/RIU and −50 pm/°C when the liquid refractive index ranges from 1.30 to 1.40 with temperature ranging from 0 °C to 100 °C. Numerical results indicate that there may not be mutual interference between two channels for measuring refractive index and temperature, which reduces the complexity of sensor measurements.

Based on the nonlinear saturable absorption properties (NSAPs) of a two-dimensional (2D) material of antimony selenide (Sb2Se3), a Q-switched erbium-doped fiber (EDF) laser is systematically demonstrated. The Sb2Se3 nanosheets are prepared by liquid-phase exfoliation (LPE) method. After the sandwich-structured Sb2Se3 saturable absorber (SA) is fabricated, the NSAPs are characterized and the modulation depth, the saturation intensity and the unsaturated loss are determined to be 25.2%, 2.02 MW/cm2, and 3.29%, respectively. When the as-prepared Sb2Se3-SA is integrated into the ring cavity, the laser operates at a stable Q-switching regime in the pump power range of 100—400 mW. The laser oscillates at the central wavelength of 1 558.48 nm with a 3 dB bandwidth of 2.32 nm. Take the advantages of the Sb2Se3-SA, the pulse duration can be compressed from 40.49 kHz to 128.12 kHz. At the pump power of 400 mW, the Q-switching laser gives the narrowest pulse duration, the highest average output power, the largest pulse energy, and the signal-to-noise ratio (SNR) of 0.93 s, 2.16 mW, 16.89 nJ, and 53 dB, respectively. Our new attempt on Sb2Se3-based Q-switched EDF laser, combining the existing mode-locking achievements, proves that Sb2Se3 is a powerful candidate for pulse compression due to the characteristics of high modulation depth and high stability.

A triple-band miniaturized end-fire antenna based on the odd modes of spoof surface plasmonic polariton (SSPP) waveguide resonator is proposed in this paper. To meet the ever increasing demand for more communication channels and less antenna sizes, multi-band antennas are currently under intensive investigation. By a novel feeding method, three odd modes are excited on an SSPP waveguide resonator, which performs as an end-fire antenna operating at three bands, 7.15—7.26 GHz, 11.6—12.2 GHz and 13.5—13.64 GHz. It exhibits reasonably high and stable maximum gains of 5.26 dBi, 7.97 dBi and 10.1 dBi and maximum efficiencies of 64%, 92% and 98% at the three bands, respectively. Moreover, in the second band, the main beam angle shows a frequency dependence with a total scanning angle of 19°. The miniaturized triple-band antenna has a great potential in wireless communication systems, satellite communication and radar systems.

A novel suppression method of the phase noise is proposed to reduce the negative impacts of phase noise in coherent optical orthogonal frequency division multiplexing (CO-OFDM) systems. The method integrates the sub-symbol second-order polynomial interpolation (SSPI) with cubature Kalman filter (CKF) to improve the precision and effectiveness of the data processing through using a three-stage processing approach of phase noise. First of all, the phase noise values in OFDM symbols are calculated by using pilot symbols. Then, second-order Newton interpolation (SNI) is used in second-order interpolation to acquire precise noise estimation. Afterwards, every OFDM symbol is partitioned into several sub-symbols, and second-order polynomial interpolation (SPI) is utilized in the time domain to enhance suppression accuracy and time resolution. Ultimately, CKF is employed to suppress the residual phase noise. The simulation results show that this method significantly suppresses the impact of the phase noise on the system, and the error floors can be decreased at the condition of 16 quadrature amplitude modulation (16QAM) and 32QAM. The proposed method can greatly improve the CO-OFDM system's ability to tolerate the wider laser linewidth. This method, compared to the linear interpolation sub-symbol common phase error compensation (LI-SCPEC) and Lagrange interpolation and extended Kalman filter (LRI-EKF) algorithms, has superior suppression effect.

Chemical oxygen demand (COD) is an important criterion for detecting the emission of pollutants and judging the quality of water. This paper improves the absorption spectrum compensation model for COD and turbidity mixed solution in the dual-wavelength spectral method based on the Lambert-Beer law additive principle. It compensates for the influence of turbidity on the absorption coefficient of the COD solution at 355 nm by the absorption spectrum coefficient of the mixed solution at 623 nm. This paper establishes a linear relationship model between the absorbance difference of the mixed solution at 355 nm and 623 nm and COD. The experimental determination coefficient R2 of the model is 0.983 35, with a relative error of 3.5% and an average error of 0.7 mg/L. The design of the model is simple and easy to systematize, which is of strong significance for practical application.

Current you only look once (YOLO)-based algorithm model is facing the challenge of overwhelming parameters and calculation complexity under the printed circuit board (PCB) defect detection application scenario. In order to solve this problem, we propose a new method, which combined the lightweight network mobile vision transformer (MobileViT) with the convolutional block attention module (CBAM) mechanism and the new regression loss function. This method needed less computation resources, making it more suitable for embedded edge detection devices. Meanwhile, the new loss function improved the positioning accuracy of the bounding box and enhanced the robustness of the model. In addition, experiments on public datasets demonstrate that the improved model achieves an average accuracy of 87.9% across six typical defect detection tasks, while reducing computational costs by nearly 90%. It significantly reduces the model's computational requirements while maintaining accuracy, ensuring reliable performance for edge deployment.

Aiming at the problems of low detection accuracy and large model size of existing object detection algorithms applied to complex road scenes, an improved you only look once version 8 (YOLOv8) object detection algorithm for infrared images, F-YOLOv8, is proposed. First, a spatial-to-depth network replaces the traditional backbone network's strided convolution or pooling layer. At the same time, it combines with the channel attention mechanism so that the neural network focuses on the channels with large weight values to better extract low-resolution image feature information; then an improved feature pyramid network of lightweight bidirectional feature pyramid network (L-BiFPN) is proposed, which can efficiently fuse features of different scales. In addition, a loss function of insertion of union based on the minimum point distance (MPDIoU) is introduced for bounding box regression, which obtains faster convergence speed and more accurate regression results. Experimental results on the FLIR dataset show that the improved algorithm can accurately detect infrared road targets in real time with 3% and 2.2% enhancement in mean average precision at 50% IoU (mAP50) and mean average precision at 50%—95% IoU (mAP50-95), respectively, and 38.1%, 37.3% and 16.9% reduction in the number of model parameters, the model weight, and floating-point operations per second (FLOPs), respectively. To further demonstrate the detection capability of the improved algorithm, it is tested on the public dataset PASCAL VOC, and the results show that F-YOLO has excellent generalized detection performance.

Research on human motion prediction has made significant progress due to its importance in the development of various artificial intelligence applications. However, effectively capturing spatio-temporal features for smoother and more precise human motion prediction remains a challenge. To address these issues, a robust human motion prediction method via integration of spatial and temporal cues (RISTC) has been proposed. This method captures sufficient spatio-temporal correlation of the observable sequence of human poses by utilizing the spatio-temporal mixed feature extractor (MFE). In multi-layer MFEs, the channel-graph united attention blocks extract the augmented spatial features of the human poses in the channel and spatial dimension. Additionally, multi-scale temporal blocks have been designed to effectively capture complicated and highly dynamic temporal information. Our experiments on the Human3.6M and Carnegie Mellon University motion capture (CMU Mocap) datasets show that the proposed network yields higher prediction accuracy than the state-of-the-art methods.

Smart grid substation operations often take place in hazardous environments and pose significant threats to the safety of power personnel. Relying solely on manual supervision can lead to inadequate oversight. In response to the demand for technology to identify improper operations in substation work scenarios, this paper proposes a substation safety action recognition technology to avoid the misoperation and enhance the safety management. In general, this paper utilizes a dual-branch transformer network to extract spatial and temporal information from the video dataset of operational behaviors in complex substation environments. Firstly, in order to capture the spatial-temporal correlation of people's behaviors in smart grid substation, we devise a sparse attention module and a segmented linear attention module that are embedded into spatial branch transformer and temporal branch transformer respectively. To avoid the redundancy of spatial and temporal information, we fuse the temporal and spatial features using a tensor decomposition fusion module by a decoupled manner. Experimental results indicate that our proposed method accurately detects improper operational behaviors in substation work scenarios, outperforming other existing methods in terms of detection and recognition accuracy.