In this paper, we present a tri-band electromagnetically induced transparency-like (EIT-like) terahertz metamaterial antibiotic sensor based on multi-mode coupling, incorporating a π-shaped resonator and a rectangular resonant ring to generate three distinct transparency windows centered at 0.76, 0.79, and 0.85THz. These frequencies correspond to the characteristic fingerprint spectra of chlortetracycline, tetracycline, and sodium penicillin, respectively, enabling the specific detection of each antibiotic. The sensor demonstrates excellent performance with sensitivities of 163, 93, and 155GHz/RIU, quality factors (Q) of 14.8, 18.5, and 26.5, and figures of merit (FOM) of 3.2, 2.2, and 4.8 for the three resonant peaks. Furthermore, we developed an efficient inverse design method using a one-dimensional parallel convolutional neural network (CNN) architecture, where each branch employs differently sized convolutional kernels to capture multi-scale local features. The optimized network achieves rapid convergence within 300 iterations and attains a mean squared error (MSE) of 0.026 on the validation set, demonstrating both fast and accurate design capabilities for metamaterial sensors. This work significantly advances the development of multi-frequency EIT sensors and their practical applications.

Kelp is a marine product with a strong bioaccumulation capacity, and its Cs concentration can effectively reflect both marine environmental pollution and seafood safety. In this study, LIBS spectra of kelp samples with varying Cs concentrations were acquired using a nanosecond laser under ambient air conditions. A total of five quantitative models were developed, including a univariate calibration curve (CC) model and four multivariate PLSR models based on different spectral preprocessing methods (Raw, SNV, Total, and Max), to achieve accurate prediction of Cs concentrations in kelp. For raw spectra, the CC model yielded a calibration curve with an R2 of only 0.7866, and predictions for 400 and 800ppm samples exhibited RE of 18.88% and 22.42%, respectively. By contrast, the Raw-PLSR model substantially improved predictive performance, reducing RE to 2.59% and 7.40% for the test-set samples. Further enhancement was achieved with spectral preprocessing, which improved both accuracy and precision. Among the four PLSR models, SNV-PLSR demonstrated the best overall predictive performance. Although its RMSEC was slightly higher than that of Total-PLSR, it produced the lowest RMSEP-reduced by 13.57% relative to the other three models-and the highest R2 (0.9802). Consistently, SNV-PLSR yielded the most accurate predictions at both low and high concentrations, with RE values of 0.02% and 2.34%, respectively. In terms of stability, all four PLSR models showed relatively high RSDs for the 400ppm sample, reflecting the challenges of low-concentration prediction; nevertheless, SNV-PLSR achieved the lowest RSD (18.58%). For the 800ppm sample, all models performed well, with RSDs below 13.25%. Additionally, three other multivariate methods (RF, PCR, and SVR) were also employed to construct new models by combining different spectral preprocessing methods. The model combinations with the best performance in terms of calibration and prediction indicators were selected and compared with the SNV-PLSR model. The results of the RMSE, R2, and test-set MRE further demonstrated the superiority of the PLSR model in the field of multivariate analysis methods. These results demonstrate that LIBS combined with PLSR and appropriate spectral preprocessing can significantly enhance the accuracy and stability of Cs quantification in kelp, providing a promising approach for rapid detection of heavy metals in marine products and monitoring of marine environmental pollution.

This publisher's note reports corrections to Appl. Opt.64, 9391 (2025).APOPAI0003-693510.1364/AO.568842.

Imaging under spatially incoherent illumination remains a key challenge due to resolution loss from diffraction-limited point-spread functions (PSFs). We investigate engineered PSFs generated by two fractional vortex phase masks, the anomalous multiramp phase plate, and the elliptical fractional vortex phase plate, applied here for the first time, to our knowledge, in incoherent imaging. These phase domain-coded apertures introduce controllable phase discontinuities, singularity distributions, and anisotropic diffraction features, enabling flexible PSF shaping through fractional topological charge, ramp geometry, and ellipticity. We analyze how parameter variations transform PSF structures, from compact spiral lobes to extended asymmetric patterns, and discuss their implications for spatial encoding, directional selectivity, and depth discrimination. To overcome the reconstruction challenges posed by such PSFs, we propose a frequency regularized nonlinear reconstruction (FR-NLR) algorithm that incorporates spectral priors for enhanced stability. Quantitative and qualitative evaluations show that FR-NLR consistently outperforms the nonlinear reconstruction (NLR), Lucy-Richardson-Rosen algorithm, and Wiener deconvolution, achieving compact, high-fidelity, robust, and scan-free incoherent imaging.

In underwater wireless optical communication (UWOC) systems, using single-photon avalanche diodes (SPAD) as a detector can improve the detection sensitivity and thus improve the transmission distance. However, the signal detection for SPAD-based systems is greatly challenged by the complex optical channel characteristics and SPAD nonlinear distortion. In order to mitigate the nonlinear distortion caused by SPAD detectors, signal-dependent noise (SDN), and the effect of underwater channel attenuation on the transmitted signal in UWOC systems, this paper proposes a signal detection scheme using deep neural networks with embedded SDN variance (DNN-SDN). This scheme performs joint equalization and demodulation of the received signal, avoiding information loss and error propagation between these two processes, thereby achieving more accurate signal recovery. In this work, we first characterize the statistical noise model for the SPAD array and establish a nonlinear relationship between the number of detected photons and the transmitted signal power. The SDN variance is integrated as an input feature during network training. Performance is quantified via a joint loss function with parameters optimized through a systematic ablation study. Finally, the performance of the proposed signal detection scheme is also compared with other schemes in different waters. Simulation results demonstrate that the scheme can effectively mitigate the effects of SPAD-induced nonlinear distortion and SDN on the signal, and the proposed method extends the transmission distance by 20%-40%.

In fringe projection profilometry, system calibration serves as a critical prerequisite for ensuring measurement precision. During calibration procedures, the elimination of specular reflection-induced saturated image poses significant challenges due to inherent surface reflectance variations. Therefore, a calibration technique utilizing adaptive polarization fringe projection is proposed in this paper. The proposed method analyzes gray-level information model from the polarization camera and employs a dichotomizing search strategy to determine optimal polarization projection intensity for saturated pixels. Furthermore, precise correspondence between image pixels and projection pixels is established. Then, adaptive polarization projection fringe patterns are generated. The proposed method enables the acquisition of high-quality calibration images, effectively enhancing system calibration accuracy. Compared with conventional calibration methods under natural conditions, the proposed method improves calibration accuracy while achieving a 9.6% improvement in measurement precision.

In order to solve the video shaky problem of underwater fishing net video suffering from shooting equipment and water flow disturbance, a multi-stage video stabilization algorithm on SE(2) is proposed. We integrate low-rank sparse decomposition, deep feature detection, and log-space trajectory optimization. First, Iterated Robust CUR is used to decompose the video sequence into foreground and background components, extracting the dynamic fishing net region as a mask. Second, the SuperPoint feature detector is adopted to find high-confidence keypoints. The keypoints are then combined with mask and Lucas-Kanade optical flow tracking to construct motion trajectory. Finally, global motion parameters are optimized by smoothing the L1 norm trajectory of SE(2) in logarithmic space. The experimental results show that our method performs better in both quantitative and qualitative analyses compared to existing methods.

The peculiar properties of the exceptional point (EP) in a non-Hermitian metasurface have attracted tremendous attention in many fields: optics, acoustics, photonics, quantum computing, and biomedicine, among others. The integration of terahertz spectroscopy's high-resolution and non-destructive attributes with perturbation-induced frequency shift responses near the EP demonstrates well the potential applications of EP sensing in various detection scenarios. Herein, we propose a non-Hermitian terahertz metasurface design with a unit cell composed of metallic elements (a strip and a split ring) and a conductivity-tunable vanadium oxide (VO2) strip. By tuning the geometrical parameters and modulating the VO2 conductivity, we systematically investigate the dynamic responses of the metasurface at the EP. Importantly, a metasurface-based refractive index sensing scheme using EP is demonstrated via the frequency shift and temperature variation after EP reconfiguration. Our work on sensing using EP could facilitate the design of tunable on-chip optical sensors for the terahertz range.

This paper presents an optical modification of the method of wireless byte-by-byte transmission of a digital signal multiplexed by the value of the orbital angular momentum (OAM) of an electromagnetic wave. This method was previously applied to radio signals. The transmitter is made as a circular array of coherent laser beams formed by splitting the original laser radiation into eight channels with different OAM values. The wave field propagates in space and comes to the input of the receiving telescope, and a sorter of OAM modes is used to demultiplex the signal. The combining of eight laser beams in the comparator yields the complete structure of the transmitted digital signal. A conceptual design of the experimental setup implementing this method is proposed, and the possibility of its practical implementation is demonstrated through numerical simulation.

We propose a terahertz continuous-variable quantum key distribution protocol using pulse position modulation, which enables simultaneous quantum and classical communication for inter-satellite links. In this scheme, classical information is conveyed through the relative positioning of encoded pulses while quantum information is encoded using Gaussian modulation to generate thermal terahertz (THz) signals. Coherent detection is employed to measure all the relevant parameters. Extensive multi-parameter simulations were conducted to validate the protocol, examining the bit error rate, secret key rate, and transmission distance under varying pulse position modulation (PPM) settings and other parameters. The results demonstrate that the protocol is feasible and represents an effective method for quantum key distribution. The proposed approach offers a promising pathway for the deployment of inter-satellite quantum communication networks.