We propose a refractory broadband absorber in hexagonally arrayed TiN–SiO₂–TiN metamaterial. Through manipulating optical modes and their coupling, we obtain broadband absorber with absorptivity over 80% in visible to near infrared (NIR) region. The flat absorption band forms with surface lattice resonance as low wavelength band edge, vertical gap plasmons as upper wavelength band edge, and the horizontal gap plasmons leverage the middle of the band. The intrinsic loss of TiN film at the bottom layer also provides great contribution of high absorption in short wavelength. Besides, the use of refractory material allows absorber to work in hard environment such as high temperature, strong erodible and high-power laser incidence, etc. All these properties would make the refractory broadband absorber have potential usage in diversity optical devices.

Optical time domain reflectometer (OTDR) is an instrument that utilizes optical fiber backscattered and Fresnel reflection to detect fiber attenuation characteristics. With the development of 5G optical communication, the effective maintenance and fault detection of long-distance optical fiber links using OTDR technology have become a research hotspot. This paper proposes a method to enhance the dynamic range by combining Simplex coding and complementary correlated Prometheus orthonormal sequence (CCPONS) coding, thereby extending the fiber detection range beyond that achievable with a single coding scheme.

In order to thoroughly investigate the impact of lasers on the image quality of charge-coupled device (CCD) image sensors, this paper employs objective image quality assessment methods to study the damage effects of 1 064 nm continuous lasers on CCD image sensors at different power densities. Through a comprehensive analysis of grayscale distribution histograms, peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM), the variations in image quality with increasing power density are revealed. The research results indicate that at low power density levels, the image quality decreases slowly; however, once the power density reaches a threshold, the image quality rapidly declines. When the image quality is severely damaged, the frequency of the main grayscale value drops to 0, and the degree of image damage saturates. Additionally, with the increase in power density, the dynamic range of the image first expands and then shrinks, eventually shifting towards the low grayscale value range.

GaInP/GaAs/Ge triple-junction solar cells have been widely used in satellite applications. The existence of high energy protons in deep space exploration will inevitably degrade the performance of the solar cells. We have conducted irradiation experiments with proton energies of 80 MeV and 100 MeV, and found that after irradiation with 80 MeV and 100 MeV protons, short-circuit current (I_sc) of the triple-junction cell remains essentially unchanged, while open-circuit voltage (V_oc) degrades to 93% of its value before irradiation. With technology computer aided design (TCAD) simulations, we have found that the top cell GaInP contributes the most to the overall degradation of the V_oc, while the middle cell GaAs contributes the most to the overall degradation of the I_sc; with 100 MeV proton irradiated on GaInP/GaAs/Ge triple-junction solar cells, the hole concentration in the top cell junction decreases from the non-irradiated level of 1.47×10¹¹ cm⁻³ to 4.12×10⁹ cm⁻³, leading to significant degradation of the V_oc in the triple-junction cell. These results will aid in understanding the degradation behavior of characteristics in GaInP/GaAs/Ge triple-junction solar cells and their intrinsic relationship with irradiation damage in sub-cell.

A nanocrystal-molecule complex composed of CdSe donor and Rhodamine B (RhB) acceptor is prepared to investigate the effect of accepter-donor ratio on the Förster resonance energy transfer (FRET) process. To highlight the FRET process, the energy level alignment between CdSe and RhB is purposefully designed and CdSe nanocrystal is coated with a wide band gap ZnS shell. The carrier dynamics is observed via combined spectral analysis. The results reveal clear FRET process between the CdSe donor and RhB acceptor. The FRET is enhanced by increasing RhB/CdSe ratio and a gradual saturation will be present at high RhB concentration.

To address the issue of temperature cross-sensitivity in refractive index (RI) measurement, a fiber-optic surface plasmon resonance (SPR) sensor based on dual D-shaped no-core fiber (NCF) is proposed and designed. The sensor mainly consists of D-shaped NCF, Ag/Fe₂O₃ composite film, and polydimethylsiloxane (PDMS), where the silver and Fe₂O₃ are successively coated on both D-shaped surfaces of NCF to generate SPR and enhance the oxidation resistance. The layer of PDMS coated on one D-shaped surface acts as a tunable ambient medium sensing the temperature variation and affecting the transmission spectrum of the sensor. Since the surface plasmon waves are excited in the two D-shaped surfaces with different films, the two RI and temperature-dependent spectral resonance peaks with different wavelengths will be produced. Research results demonstrate that the two peaks’ wavelengths have different responses to the variations of ambient RI and temperature. In the RI range of 1.36–1.376 and temperature range of 10–100 °C, the RI and temperature sensitivities reach 5 000 nm/RIU and −0.546 nm/°C, respectively. With the sensitivity coefficient matrix, simultaneous measurement of RI and temperature can be realized, and the corresponding accuracy is superior to 0.4×10⁻⁵ RIU and 0.037 °C.

Previous point-wise methods are suffering from time consumption and limited receptive fields to capture information among points. To address these limitations, we propose the cosh-attention, which reduces the computation complexity of space and time from the quadratic order to linear order with respect to the number of points. In the cosh-attention, the traditional softmax operator is replaced by non-negative ReLU activation and hyperbolic-cosine-based operator with re-weighting mechanism. Then based on the key component, cosh-attention, we present a two-stage hyperbolic cosine transformer (ChTR3D) for 3D object detection from point clouds. It refines proposals by applying cosh-attention in linear computation complexity to encode rich contextual relationships among points. Extensive experiments on the widely used KITTI dataset and Waymo Open Dataset demonstrate that compared with vanilla attention, the cosh-attention significantly improves the inference speed with competitive performance. Among two-stage state-of-the-art methods using point-level features for refinement, the proposed ChTR3D is the fastest one.

To improve the accuracy of event-based gait recognition, a method called voxel event graph neural network (VEGNN) is proposed. This method voxelizes the event stream and selects representative voxels as vertices of the graph. Then the edges are constructed based on spatio-temporal distance and temporal order constraints so that the event stream is constructed as a graph structure. Finally, a lightweight feature extraction network based on graph neural networks (GNNs) is used to efficiently capture spatio-temporal information and motion cues from the event graph. To evaluate our method, an event-based gait recognition dataset called Celex-Gait is created. Experimental results show that VEGNN achieves a gait recognition accuracy of 95.8% and 93.9% on the Celex-Gait dataset and the DVS128-Gait-Day dataset, respectively. Furthermore, compared to the state-of-the-art methods, VEGNN reduces the number of model parameters by 30%. This indicates that VEGNN achieves higher recognition accuracy with lower model complexity.

The network video training mode of five step fist has become the mainstream training mode at present, because it is not limited by time and region. However, there are also some problems, such as irregular movements of practitioners, due to the influence of subjective human eyes, which will damage their health. Therefore, this paper proposes a pose recognition and correction method of the five step fist based on OpenPose. By identifying the standard movements and the movements of the practitioners, the key points are extracted, the deviation and similarity between the practitioners and the standards are calculated, and the corresponding correct and effective correction suggestions are given. These works can provide strong technical support for the training and cultural dissemination of the five step fist.

To address the issues of low accuracy, false positives, and missed detections in conventional blood cell detection methods, we propose TBV-YOLO, an improved blood cell detection algorithm based on the YOLOv7 network. This algorithm incorporates our lightweight feature extraction module, BP-ELAN. By employing bi-level routing attention (BRA) and task-specific context decoupling (TSCODE), TBV-YOLO enhances the extraction of key local features and introduces richer semantic information, thereby improving the detection accuracy for dense and small targets. Additionally, the use of partial convolution (PConv) and the VoVGSCSP lightweight feature fusion module reduces computational complexity, further lightening the network model. Experimental results on the blood cell count and detection (BCCD) dataset demonstrate that the proposed model achieves a mean average precision at intersection-over-union (IoU) of 0.5 (mAP@0.5) of 93.9%, precision of 86.2%, and recall of 94.1%, representing improvements of 2.6%, 1.8%, and 5.7% over YOLOv7, respectively, with a model parameter size of only 8.8M.

Colorectal cancer (CRC) is a prevalent disease, with polyps serving as its precursors. Accurate polyp segmentation is crucial for early CRC prevention. However, due to different sizes of the polyps, the boundaries are not clear. Therefore, accurate segmentation of polyps is a challenging task. This paper proposes vision Mamba attention feature fusion UNet (VMA-UNet), a U-shaped asymmetric codec structure model grounded in the state space model (SSM). The VMA-UNet incorporates attention feature fusion (AFF) in order to enhance the feature representation of small polyps. A new IUD loss function, namely combining intersection over union (IoU) loss function and Dice loss function, is proposed to address both large polyps and small polyps, and to mitigate the issue of data imbalance. When applied to multiple datasets, VMA-UNet demonstrates robust performance, particularly in small polyp segmentation, showcasing its practical value. The network proposed in this paper overcomes the inherent shortcomings of convolutional neural network (CNN) and transformers, not only performing well in remote interaction modeling, but also maintaining linear computational complexity. Our study introduces a new method for polyp segmentation based on SSM and advances the field.