Optical Review最新文献

A Mach–Zehnder interferometer (MZI) based on the two-end etching of multi-core fiber (MCF) is proposed and experimentally demonstrated. The MZI consists of a seven-core fiber with two-end etching and is sandwiched in two single-mode fibers. To study the influence of different etching time on MZI, BOE solution is used to etch MCF. Due to the fact that BOE solution is a mixture of HF and NH₄F, the etching rate of BOE on the MCF is slower, which is more conducive to observing the etching appearance of the MCF. The effect of etching on MZI spectrum is analyzed theoretically. Experimental results confirm and show that the humidity increases from 25%RH to 95%RH, the MZI energy monitoring point decreases, and its humidity response sensitivity is − 0.045 dB/%RH. Since the temperature also affects the sensor response, the temperature is also measured. The temperature increases from 35℃ to 85 ℃, and the MZI temperature response sensitivity is 0.0465 nm/℃. A quick and reliable time response has also been demonstrated and shows potential for future applications.

This paper presents several ultrathin multi-band terahertz bandpass filters (BPFs) based on metal-dielectric-metal (MDM) metasurfaces, achieving independently tunable multi-band transmission characteristics through parametric adjustment of the geometric parameters (side length w, gap spacing (varDelta s), and loop count N) of the square-loop arrays. The designed tri-band filter is primarily analyzed for its excellent performance at 0.72/1.23/2.23 THz, including a roll-off rate >30 dB/THz, average transmittance of 97% (peak insertion loss (< -0.10) dB), and out-of-band rejection ratio >25 dB. The multi-band spectral response is validated via finite integration technique (FIT) simulations in CST Microwave Studio, combined with surface electric field distribution analysis and effective medium theory (EMT) to elucidate the propagation mechanism of THz waves and the functional roles of each structural layer. A systematic investigation of geometric parameter dependencies on transmission performance is also conducted. The designed BPF exhibits potential applications in electromagnetic interference suppression, ultrasensitive biosensing, and dynamic THz signal multiplexing, owing to its multi-band transmission, polarization insensitivity, and compact footprint.

Thermal imaging cameras operate effectively at night or in low-light conditions, enhancing the accuracy of human pose recognition in such environments. To address the issue of insufficient accuracy in traditional human pose recognition methods under low-light conditions, this study proposes an improved algorithm based on the YOLOv8 model, referred to as YOLOv8-SLG. Considering the peculiarity that thermal images reflect temperature information rather than optical features, traditional convolutional networks may produce unnecessary redundancy in feature extraction. To address this issue, first, we improve the detection accuracy and preserve the network structure by replacing the original convolution with SCConv building blocks in the backbone network of YOLOv8n in order to reduce spatial and channel redundancy between features in the convolutional neural network. Second, we enhance local feature detection by integrating the LSKA attention mechanism into the neck network, reducing computational complexity and memory requirements while maintaining accuracy. Finally, we enhance the multi-scale processing capability and reduce the number of parameters per detection head through shared GroupNorm convolution to improve target localisation and classification performance. Experimental results show that these enhancements significantly improve the model’s performance for human pose recognition tasks in complex contexts. Compared to the original YOLOv8n model, the proposed algorithm improves the precision, recall, mAP50, and mAP50-95 metrics by 1.33%, 1.79%, 1.86%, and 2.01% to 97.7%, 94.6%, 96.7%, and 75.1%, respectively. In addition, YOLOv8-SLG reduced model parameter calculations by 8.14%. It can detect human poses in thermal images in real time accurately, and comparison with other mainstream human pose detection algorithms confirms the effectiveness and superiority of the method.

In this paper, a single-polarized photonic crystal fiber(PCF) filter for the communication band was proposed. The polarization properties of PCF with a single gold-coated liquid-filled hole have been investigated by the full vector finite element method(FEM). Extensive simulations reveal that near the 1310 nm communication wavelength, the y-polarized core mode exhibits a significantly higher peak loss intensity of 453.46 dB/cm compared to the x-polarized core mode's substantially lower value of 2.068 dB/cm. With a length of 500 μm of the designed PCF, the bandwidths of the crosstalk less than -20 dB can reach to 213 nm. Fiber filters can accurately implement the filtering function for a single communication window. It is expected to play a key role in optical communication, optical sensing and other related fields, providing strong support for technological development and application expansion in these fields.

This article proposes an elliptical photonic crystal fiber (PCF) structure for broadband polarization filtering and refractive index (RI) sensing. By employing an elliptical core and elliptical air holes, the design achieves a high resonance intensity for y-polarization of 789 dB/cm, with a corresponding x-polarization loss of 0.29 dB/cm at a communication wavelength of 1.31 μm, resulting in a polarization extinction ratio of 2721. For a fiber length of 200 μm, the structure attains a crosstalk (CT) value of 137.0 dB at 1.31 μm. Furthermore, it exhibits a bandwidth of 1.04 μm with CT levels exceeding 20 dB, spanning the wavelength range from 1.16 to 2.2 μm. The same elliptical PCF structure can also function as a RI sensor, achieving a sensitivity of 8600 nm/RIU within a sensing range of 1.29–1.31. Additionally, replacing the elliptical holes with fully circular holes in the proposed fiber maintains a wide polarization-filtering bandwidth and yields a high RI sensitivity of 21,400 nm/RIU over the range 1.37–1.39. These results indicate that fully circular holes based on the proposed PCF structure represent a viable alternative for applications in both polarization filtering and RI sensing.

This paper explores the propagation characteristics of Airyprime beams in an inhomogeneous medium with periodic potential, from both theoretical and numerical simulation perspectives. By using the method of separation of variables, the Gross–Pitaevskii equation with periodic potential was solved to obtain the breather soliton solution and breathing period. Additionally, considering the medium and beam parameters, numerical simulations were performed to study the propagation characteristics of Airyprime beams and the superposition between two Airyprime beams. First, the influence of initial medium parameters (modulation intensity P and modulation frequency ω) on the propagation characteristics was studied. Then, the effect of initial beam parameters (initial chirp C and position x₀) on the propagation characteristics was analyzed. Finally, the superposition between two Airyprime beams with different phases (varphi = 0) (varphi), amplitudes A, and initial separations x₀ was investigated. By changing the initial medium parameters, the breathing period and central position of the breather soliton can be controlled; by adjusting the initial beam parameters, the deflection direction, size, and maximum intensity of the breather soliton can be manipulated. Changing the phase (varphi = 0) (varphi), amplitude A, and initial separation x₀ of the two Airyprime beams can form different bound-state breather solitons. The results provide a theoretical foundation for the propagation and control of Airyprime beams, as well as for their potential applications in optical communication.

In this paper, a dual hollow-core anti-resonant fiber polarization beam splitter (DHC-ARF PBS) with ultra-wide splitting bandwidth is proposed. The effects of the structure parameters of the DHC-ARF PBS on the splitting performances, including the coupling length, coupling length ratio, confinement loss, and higher-order mode extinction ratio (HOMER), are investigated using the finite element method. The simulation results show that under the optimal structure parameters, the proposed DHC-ARF PBS has larger HOMER (> 100) within the working wavelength range, indicating its good single-mode transmission characteristics. Moreover, the proposed DHC-ARF PBS has a short splitting length of 2.07 cm and an ultra-wide splitting bandwidth of 610 nm (1320 ~ 1930 nm), which covers the whole E, S, C, L, and U bands and a portion of O band. It is believed that the proposed DHC-ARF PBS will have significant applications in the optical communication and sensing systems.

Hyperspectral (HS) imaging, which acquires the detailed spectral information of an object, has attracted extensive interest in various fields, such as remote sensing, agriculture, and biomedicine. In the dawn of HS imaging, HS imaging relied primarily on time-consuming scanning techniques to achieve high spatial and spectral resolution, limiting its applicability. Subsequently, snapshot (non-scanning) HS imaging emerged, enabling high-speed capturing. The snapshot method captures HS images in a single exposure, but it has the drawback of lower spatial resolution. With recent advancements in computational technology, high-efficiency HS imaging has been pursued in cooperation with image post-processing. In this review, the historical evolution of HS imaging is described focusing on spectroscopic techniques. An up-to-date HS imaging technique, computational HS imaging using a spatial-spectral random coded mask, is introduced with a brief explanation of its working principle. The HS imaging technique demonstrates outstanding characteristics in terms of sensitivity, spatial resolution, spectral accuracy, and frame rate. Several applications of HS imaging are introduced showing improved accuracy of image analysis compared to traditional RGB image analysis. Finally, the remaining challenges and prospects are discussed.

Underwater Optical Wireless Communication (UOWC) is a key enabler of the Internet of Underwater Things (IoUT), offering high-speed and low-latency data transmission in aquatic environments. However, system performance is highly sensitive to environmental factors such as absorption, scattering, turbulence, and noise. This study presents a comprehensive, simulation-based performance analysis and optimization of UOWC systems using Opti-System software, focusing on laser diodes operating at 450 nm and 520 nm. Four representative water types—pure seawater, clear ocean, coastal ocean, and turbid harbor—are examined to evaluate key performance metrics, including received power, Q-factor, eye diagrams, signal-to-noise ratio (SNR), bit error rate (BER), and transmission range. An environment-aware optimization framework is proposed, correlating wavelength selection and system configurations with the optical properties of each water type. The impact of transmitter power, beam divergence, photodetector sensitivity, and modulation (OOK) is assessed to identify optimal design choices for robust communication. Simulation results reveal that shorter wavelengths perform better in clearer waters, while adaptive tuning strategies significantly enhance reliability in highly scattering or turbulent conditions. This work provides practical design insights and optimization strategies to support scalable, efficient, and resilient UOWC systems for real-world IoUT applications.

To compensate for time-fluctuating spatial noise and reconstruct an object image, a deep learning-based single-pixel imaging (SPI) system using a neural network consisting of five transposed convolutional layers and three convolutional layers has been developed. In the present study, we proposed a new image reconstruction method using deep learning with a feature-extended time-division pattern-learning (TDPL) network, which further increased the number of features in each layer to enhance the tolerance to time-fluctuating spatial noise. Simulations and experiments were performed to compare the performance of the proposed network with that of conventional methods, such as computational ghost imaging, Hadamard single-pixel imaging, deep convolutional auto-encoder network (DCAN), and TDPL network. We found that the image quality of the reconstructed image using the proposed method is superior to that of conventional methods in any environment with time-fluctuating spatial noise. For example, the quality of an object image reconstructed using the proposed method improved by − 0.037 and − 0.014 in a root-mean-square error and + 0.083 and + 0.005 in a structural similarity compared to that using the DCAN and TDPL network, respectively, under time-fluctuating spatial noise with a standard deviation of 0.5. Therefore, the proposed deep learning-based SPI system with a feature-extended TDPL network is expected to be applied to various imaging or observation in an environment where conditions are likely to change, such as astronomical observations, remote monitoring, and optical wireless communications.