Continuously programmable dielectric materials are increasingly important for integrated photonics, multilayer optical structures, and refractive index-engineered photonic architectures. However, achieving predictable optical behavior in multicomponent oxides remains challenging because nanoscale compositional ordering may lead to the formation of scatterers or unpredictable local variations of effective index. Integrated photonics demands low loss and optically predictable materials. Here we demonstrate a sequence-programmed plasma-enhanced atomic layer deposition strategy for optical-property engineering in SiO$_2$--TiO$_2$ hybrid oxides. Symmetric short-period deposition sequences restrict consecutive growth of each constituent, enabling effective-medium-like optical behavior across the full composition range. Spectroscopic ellipsometry reveals continuously tunable refractive indices across the visible and near-infrared range. Prism-coupler measurements further show low-dB/cm guided-mode attenuation at visible wavelengths, with selected measurement conditions approaching the sub-dB/cm regime, together with composition-dependent effective thermo-optic response. Raman spectroscopy and atomic force microscopy indicate that SiO$_2$ incorporation suppresses or delays TiO$_2$ crystallization and preserves nanoscale surface smoothness after annealing at 500~$^\circ$C. These results demonstrate sequence-programmed ALD as a scalable optical-material platform for refractive index matching, low-loss dielectric photonics, and integrated photonic architectures requiring continuously tunable optical properties.
With the rapid development of electromagnetic technology, 5G communication, intelligent devices and military stealth technology, there is an ever-growing demand for high-performance microwave absorbing materials. Benefiting from low density, high electrical conductivity, excellent structural design flexibility and superior chemical stability, carbon-based materials have become a mainstream research hotspot in the field of microwave absorption. This paper systematically reviews the latest advances in carbon-based microwave absorbing materials, covering traditional carbon materials, novel nanocarbon materials, transition metal carbides and their multi-component composites. The fundamental microwave absorption mechanisms are elaborated, and the effects of material composition and microstructure on microwave absorption performance are analyzed in depth. Their application prospects in various fields are also summarized. Furthermore, the existing key challenges, such as inferior low-frequency absorption and restricted effective absorption bandwidth, are highlighted. Finally, the future development trends are forecasted. Multifunctional integration, ultra-wideband absorption, eco-friendly low-carbon fabrication, and precise regulation of loss mechanisms are regarded as major research directions. This review provides a theoretical basis and technical guidance for the optimal design, performance enhancement and practical engineering applications of carbon-based microwave absorbing materials.
Binocular stereo vision features low cost and non-contact sensing, making it a mainstream approach to acquire 3D scene information in fields such as 3D reconstruction and autonomous driving. As its core module, stereo matching is the key determinant of system sensing accuracy and efficiency. This paper presents a comprehensive review of binocular stereo matching. It systematically elaborates the imaging principle of binocular stereo vision and clarifies the pivotal role of stereo matching in 3D reconstruction. According to technical development trends, existing stereo matching algorithms are classified into traditional methods and deep learning-based methods. This paper also summarizes two mainstream categories of datasets: real-scene datasets and synthetic datasets. Combined with mainstream evaluation metrics including end-point error and bad pixel rate, a standardized evaluation system for stereo matching algorithms is established. Finally, the current challenges are analyzed, such as poor scene generalization and insufficient datasets for complex working conditions. Future research directions are further prospected, including multi-modal dataset construction and cross-domain model optimization, aiming to provide a solid reference for related theoretical research and engineering applications.
Camouflaged person detection from unmanned aerial vehicle (UAV) views is important for intelligent surveillance, search and rescue, and battlefield perception, yet remains insufficiently studied. This task is particularly challenging due to small target scales, cluttered backgrounds, and large viewpoint variations in UAV imagery. Existing public datasets mainly focus on conventional pedestrian scenarios or generic camouflaged objects, and therefore cannot adequately support benchmarking for UAV-view camouflaged person detection. To address this gap, we present UAV-CPD, a benchmark dataset dedicated to camouflaged person detection from UAV views, and establish a unified evaluation protocol for this task. We further propose CamouDETR, a frequency-aware end-to-end baseline built upon Dual-Branch Wavelet Enhancement (DWE), Dynamic Frequency Routing (DFR), and the Context-Aware Reference Decoder (CARD), to strengthen weak-detail representation, cross-scale frequency transmission, and context-aware box refinement. Experiments show that CamouDETR achieves 77.30 $mAP$ on UAV-CPD, outperforming RT-DETR by 3.10 points, and also improves $mAP$ on AMAD-5 from 62.16 to 65.40. These results support UAV-CPD as a useful benchmark for this task and show that the proposed baseline offers a practical starting point for UAV-view camouflaged person detection.
Accurate surface measurement and alignment are increasingly vital in industrial manufacturing, where both extended axial range and sub-micrometer precision are essential. Multi-wavelength Digital Holography (MDH) achieves this by generating synthetic phase maps through digital combination of multiple laser wavelengths. This work presents two key innovations that enhance the robustness and practicality of MDH for industrial surface metrology. First, we introduce a coherence control technique using a dynamic mode-mixing process with a phase-randomizing mirror and bent multimode fiber to suppress coherent noise by tailoring the illumination coherence length. Second, we develop a post-processing algorithm that detects and compensates for wavelength shifts using an order parameter derived from residual phase maps, enabling accurate phase unwrapping even with unstable or low-cost laser sources. Together, these contributions enable MDH to deliver reliable, phase-jump-free heightmaps with sub-micrometer accuracy over mesoscopic ranges in realistic manufacturing environments.
Ultra-short-period multilayer mirrors (period d below 5 nm) are key components for hard X-ray optics in the 20 keV-40 keV range, but their performance rapidly degrades when the period approaches a few nanometers due to density loss and/or interfacial defects (roughness or intermixing). In this work, we investigate a tungsten-rhenium alloy absorber (WRe, 50 at.% Re) in WRe/SiC multilayers and compare it with a pure W absorber. We also assess the impact of the magnetron sputtering mode, conventional direct-current magnetron sputtering (dcMS) versus high-power impulse magnetron sputtering (HiPIMS), on the optical contrast and reflectivity of WRe/SiC multilayers with d approximate to 3 nm. Four periodic stacks [Abs/SiC]40 (Abs = W or WRe) were deposited and characterized by multi-energy X-ray reflectometry from a laboratory diffractometer and synchrotron beamlines (BAMline-BESSY-II and BM05-ESRF). Simultaneous fitting of reflectivity curves at multiple photon energies yields estimates of period, densities, and interfacial roughness. This specific fitting process is based on a sequential approach, providing an accurate structure model. These results are complemented by high-resolution TEM analysis of the microstructure. Compared with W/SiC, WRe/SiC exhibits a higher absorber density and a lower spacer density (SiC). This significant increase in the density contrast between the absorber and the spacer results in higher peak reflectivity at comparable Bragg angles. Under these conditions, dense WRe layers are obtained by dcMS, and HiPIMS provides similar densification within experimental uncertainties, while preserving the interfacial quality. These results demonstrate that WRe/SiC multilayers are a promising alternative to conventional W/SiC systems for high-energy X-ray mirrors at nanometer-scale periods.
In recent years, the wide variety of digital micromirror device (DMD) applications has extended significantly across various fields of optics, including ultrafast optics. Despite these advances, the interaction between DMDs and ultrashort pulses remains poorly understood. To address this gap, this study presents a comprehensive characterization of the behaviour of a DMD system when interacting with ultrashort laser pulses. In this work, the fluence threshold for multi-shot damage was first determined to be 0.12 J/cm-2. Regarding the temporal effects, the group delay dispersion (GDD) of intrinsic materials was experimentally measured for the zeroth order and was determined to be 190 fs2. The temporal dispersion introduced by the DMD was then theoretically quantified for higher diffraction orders, showing that it generates a broadening and a spatiotemporal shift that depend on the diffraction order. Concerning spatial effects, the lateral chromatic aberration for a broad wavelength range was analysed, revealing the spatial separation of different wavelength components due to the wavelength dependence of the order of diffraction. Finally, the capability of the DMD to analyse the intensity spatial distribution of the light beam was demonstrated using a single-pixel imaging technique. These findings contribute to the understanding of the effects resulting from the interaction of ultrashort pulses with the DMD, thereby facilitating applications.
Compact mid-infrared laser generation is achieved using a demonstrated shared-cavity optical parametric oscillator (OPO) based on periodically poled magnesium-doped lithium niobate (PPMgLN). The shared-cavity design co-resonates the 1064 nm fundamental light and the similar to 1.5 mu m signal wave within a single optical cavity, thereby ensuring good spatial mode overlap and intracavity power enhancement. This configuration substantially lowers the oscillation threshold and enhances the overall conversion efficiency. The oscillation threshold at 807.6 nm is only 0.9 W. Under stable operation at a 5.0 W pump power, the system generates output at 1472.9 nm (signal) and 3833.2 nm (idler), with respective output powers of 430 mW and 187 mW. Signal linewidth is 0.9 nm, while the idler linewidth is relatively wide, 6.4 nm. The generated 3.8 mu m mid-infrared laser falls within the molecular "fingerprint" region - making it highly valuable for high-sensitivity spectral analysis and trace-gas sensing - while the 1.5 mu m signal wavelength lies within the low-loss telecommunications window.
To address the lack of dedicated datasets for infrared detection of small UAVs in air-to-air scenarios, this paper first constructs the self-built SIM-AIR dataset covering complex scenarios, and then proposes YOLO-KMM an efficient YOLOv11-based object detection model tailored to the dataset's small-target characteristics and deployment requirements; collected by an UAV equipped with an infrared thermal imager, the SIM-AIR dataset consists of 3,993 precisely annotated images across four weather conditions: sunny, cloudy, snowy, and hazy, where 99.7% of the targets are ultra-small objects and their width < 40 pixels, with an average size of 11.2 & times; 6.6 pixels, including complex scenarios such as "dark targets" in snowy weather and low signal-to-noise ratio (SNR) in haze, which fully simulate real-world detection challenges. To tackle the issues of sparse small-target features and strong background interference, YOLO-KMM integrates the C2KD feature enhancement module and C3K2-MU lightweight detection head, forming a dual-optimized architecture of "feature enhancement - efficient detection": the C2KD module captures weak small-target features and suppresses noise via cross-scale fusion and attention mechanisms, while the C3K2-MU module adopts grouped convolution and depthwise separable convolution to reduce the number of parameters while preserving feature representation capability. Experiments on the SIM-AIR dataset show that YOLO-KMM achieves an mAP(50) of 88.2%. This is 7.8% points higher than the baseline YOLOv11, with a precision of 94.0% and recall of 74.3%, reduces the small-target missed detection rate by 12.5%, and maintains an inference speed of 246.18 FPS, 2.3M parameters, and 5.4 GFLOPs of computation; compared with YOLOv5/8/12, the model achieves a better balance among accuracy, speed, and complexity, verifying the practicality and challenge of the SIM-AIR dataset and providing an efficient solution for air-to-air small-target infrared detection.
We report a high-power, high-efficiency continuous-wave Tm:KY(WO4)(2) (Tm:KYW) laser based on multimode in-band diode pumping at 1720 nm. In-band pumping reduces the quantum defect compared to conventional 800-nm pumping, enabling efficient high-power operation. We demonstrate up to 4.55 W of output power near 1.94 mu m with a slope efficiency of 83% with respect to absorbed pump power, approaching the quantum-defect limit. The laser provides smooth wavelength tunability from 1839 to 2100 nm and maintains near-diffraction-limited transverse beam quality across the full operating range. These results indicate that multimode in-band pumping of Tm:KYW is a simple and compact route to efficient, broadband, high-power sources in the 1.9-2.0 mu m region, providing a practical basis for future high-power Q-switched and mode-locked systems.
Conventional optical design relies on iterative and time-consuming optimization methods. Finding the right starting system facilitates the design of relevant optical systems. It has been demonstrated that Saddle Point Construction Method (SPCM) can be used to design innovative optical systems based on pre-existing systems, or from scratch. This paper presents the results of a Python program using Code V's Application Programming Interface (API) and applying the special version of SPCM to automatically design optical systems using a reduced glass map. To illustrate its robustness, cemented doublets have been automatically designed. A reduced glass map with thirty-four Schott glasses was combined with the SPCM for the design of 68 achromatic cemented doublets. They were then compared with achromatic cemented doublets from well-known manufacturers and with those described in the literature using a semi-analytical approach. The achromatic cemented doublets were first designed with a total field of view (FOV) of 0 degrees and were subsequently designed with a FOV of 5 degrees. The best achromatic cemented doublets obtained performed better or as well as existing achromatic cemented doublets.
This work reports the experimental demonstration of a dual-wavelength L-band fiber ring laser for remote sensing applications. The system incorporates a polarization-sensitive semiconductor optical amplifier as the gain medium and two fiber Bragg gratings placed 25 km away from the laser cavity using standard single-mode fiber that serve both as wavelength-selective elements and sensing heads. Wavelength switching between single- and dual-channel lasing configurations is enabled by a simplified two-paddle motorized polarization controller. The system achieves optical signal-to-noise ratios exceeding 55 dB and power differences between lasing lines as low as 0.01 dB. To ensure long-term stability, an automatic control algorithm dynamically adjusts the polarization state in real time, compensating for environmentally induced polarization drift. The proposed setup provides a compact and robust solution for polarization-based wavelength switching in fiber lasers, with applications in the field of remote optical sensing.
We present a numerical study of a nanocoated whispering-gallery-mode (WGM) silica microdisk as a label-free platform for single-exosome detection and refractive-index–based health-state classification. The device is modeled as a fiber-coupled silica microdisk in water, functionalized with a thin nanocoating of either polystyrene (PS) or the metal-halide perovskite CsPbI3. Using full-wave driven-mode simulations in Comsol Multiphysics, we show that nanocoatings reshape the WGM field distribution and improve sensing-relevant figures of merit by enhancing surface-field confinement while preserving high-Qf operation. Beyond the field-pulling mechanism provided by polymer coatings, we demonstrate a distinct sensitivity enhancement enabled by perovskites: spectral alignment of the WGM with an excitonic resonance in CsPbI3 supports a hybrid excitonic–photonic mode that concentrates optical energy at the sensing interface and increases the transduction of minute effective-refractive-index (ERI) variations into measurable resonance shifts. To connect the exosome composition to the optical response, we introduce a physics-based workflow to estimate dispersive ERIs of individual exosomes from their protein and nucleic-acid content using a Barer-type relation and a core–shell geometry, and we map these ERIs to resonance-wavelength shifts for single exosomes at the sensing position. The resulting resonance signatures provide separable responses for healthy-like, borderline, and cancer-like exosomes, indicating that the proposed excitonically engineered WGM microresonator can not only detect single exosomes but also classify their health state, supporting a route toward non-invasive liquid-biopsy diagnostics.
In the quest for decarbonization, industry and academia are increasing their efforts in research on innovative photovoltaic devices, and proper indoor characterization of devices in an essential element of this research, to correlate manufacturing parameters with electrical properties. For this purpose, it is essential to properly characterize artificial illumination from a solar simulator, as it might directly impact the measured quantities. To fill a literature gap, this work implements triggered high-speed spectrometers for a fast and in-situ characterization of a flashed solar simulator. A commercially available high-speed spectrometer, with a custom-made triggering circuit, was used to evaluate the impact of supply voltage on the spectral components of the solar simulator flash during its temporal evolution. This allowed to identify some spectral lines that decreased far more slowly and never turned off during the measurement window. Supply voltage, surprisingly, introduced large effects when it’s varied to adjust irradiance. Spectral Mismatch Ratio was also determined as function of time and wavelength to provide an useful parameter when using artificial radiation. These artifacts impact the measured current–voltage curves, as evaluated on three devices. This fast spectral characterization enriched the indoor characterization procedure, allowing to identify how the extinction spectra vary with time, and to identify unexpected features introduced by changes in supply voltage.
The performance of optical systems can be compromised by subsurface damage (SSD) caused by mechanical processing. This study presents a destructive method of determining SSD depth in fused silica surfaces using atmospheric plasma jet etching (PJE). A simplified mathematical model describes how the etching front evolves under the assumption of isotropic etching. By comparing simulated surfaces with topographies determined experimentally after each etching step, an isotropy factor (IF) is calculated to identify deviations from isotropy. Areas with an IF greater than one exhibit anisotropic etching behavior, indicating the presence of SSD. The method was validated using defined Vickers indentations, scratches and conventionally polished samples. The maximum SSD depth correlates with both the maximum IF value and the slope of the cavity volume versus etching depth. For the samples examined, SSD depths ranged from 6.37 μm to 52.47 μm. The results agreed well with OCT measurements (deviation <10%). The developed approach not only enables the quantitative determination of SSD depth, but also the three-dimensional reconstruction of crack morphology. Combining computer-aided modelling with experimental comparison provides a robust method for characterizing the quality of optical components.
Zernike aberration coefficients are typically presented in tabular form or as simple bar graphs, making it difficult to intuitively interpret their meaning and relate them to the specific aberration term. In this work, we present intuitive and rapidly interpretable visual representations of Zernike aberration terms that highlight the magnitude and orientation of their dominant contributions. Depending on the aberration order, we recommend different graphical formats – such as bubble plots and heatmaps – for visualizing low-order and mid-spatial frequency Zernike terms. These graphical representations are particularly valuable when quick visual feedback on individual aberration terms is needed, such as during aberration compensator adjustment, real-time wavefront visualization of dynamic processes, or optical alignment procedures. They can also enhance the clarity and interpretability of inspection reports or measurement certificates. Furthermore, an alternative definition of the azimuthal orientation of Zernike terms with m ≠ 0 is proposed, enabling a more effective analysis of the shape and bending of spoke-shaped aberrations.
We show how an interferometric setup containing a polarizing beam splitter and a slab of dispersive material allows control over the evolution of the instantaneous state of polarization of an optical pulse. With this proposed method the entire Poincaré sphere can be covered by varying the material’s thickness. The ability to control the state of polarization over the duration of the pulse may be useful in a wide variety of applications.
In recent years, the wide variety of digital micromirror device (DMD) applications has extended significantly across various fields of optics, including ultrafast optics. Despite these advances, the interaction between DMDs and ultrashort pulses remains poorly understood. To address this gap, this study presents a comprehensive characterization of the behaviour of a DMD system when interacting with ultrashort laser pulses. In this work, the fluence threshold for multi-shot damage was first determined to be 0.12 J/cm2. Regarding the temporal effects, the group delay dispersion (GDD) of intrinsic materials was experimentally measured for the zeroth order and was determined to be 190 fs2. The temporal dispersion introduced by the DMD was then theoretically quantified for higher diffraction orders, showing that it generates a broadening and a spatiotemporal shift that depend on the diffraction order. Concerning spatial effects, the lateral chromatic aberration for a broad wavelength range was analysed, revealing the spatial separation of different wavelength components due to the wavelength dependence of the order of diffraction. Finally, the capability of the DMD to analyse the intensity spatial distribution of the light beam was demonstrated using a single-pixel imaging technique. These findings contribute to the understanding of the effects resulting from the interaction of ultrashort pulses with the DMD, thereby facilitating applications.
Near-infrared optical tissue imaging is sensitive to both the optical properties of biological media and their microstructural geometry. While macroscopic tissue characteristics are well studied, the quantitative impact of skin pores on photon propagation remains largely unexplored. Here, we investigate this influence using a cascaded computational framework. Anatomically realistic tissue geometries were reconstructed from segmented MRI data of eight cadaveric heads, consisting scalp, skull, cerebrospinal fluid, and brain layers. Ballistic photon propagation simulations first resolved geometric optical interactions at the skin surface with explicit pore microgeometry, and the resulting photon states initialized Monte Carlo-based diffusive photon transport simulations to produce voxel-wise fluence maps and depth-resolved sensitivity analysis. Results indicate that skin pores increase photon angular divergence during the ballistic phase, but these directional perturbations are rapidly randomized by multiple scattering and do not measurably alter depth sensitivity (DS) profiles or photon path statistics. In contrast, pore-induced reductions in photon weight persist, decreasing the photon budget available for deeper tissue transport. These results indicate that surface microgeometry primarily affects optical coupling efficiency rather than stochastic photon propagation, providing guidance for when pore-scale features can be neglected or should be explicitly incorporated in biomedical optical system design.
Reliable detection of unmanned aerial vehicle (UAV) swarms is essential for airspace security and defense applications, however the scarcity of large-scale, densely annotated training data remains a critical bottleneck. Collecting real-world swarm data is costly, logistically challenging, and constrained by airspace regulations, while manual annotation of numerous small, fast-moving targets is time-consuming and prone to errors. To address these challenges, this paper presents SynthSwarm, a large-scale synthetic dataset specifically designed for UAV swarm detection in long-range aerial surveillance scenarios. The dataset is generated through a controllable simulation pipeline built on the Unity engine, enabling precise six-degree-of-freedom (6-DoF) pose specification for each UAV instance and automatic pixel-accurate bounding box annotation without manual labeling. SynthSwarm comprises 7000 high-resolution images (1920 × 1080) containing 31,542 UAV instances, with systematic variations in swarm density, formation patterns, target scale, and environmental conditions. Statistical analysis reveals that 67.3% of the targets qualify as small objects, reflecting the inherent difficulty of detecting distant UAV swarms. We benchmark several representative deep learning detectors, including one-stage detectors (YOLOX, YOLOv6, YOLOv12, YOLOv13), the two-stage detector Faster R-CNN, and the Transformer-based detector RT-DETR. Experimental results demonstrate that the dataset poses significant challenges for existing methods, particularly in high-density and small-target scenarios. Furthermore, cross-dataset on the MMFW-UAV dataset experiments validate the effectiveness of synthetic data as a pre-training source for improving detection performance on real UAV datasets. The dataset and generation pipeline are publicly available to facilitate further research in UAV swarm detection.
