Applied optics最新文献

Pose measurement, consisting of position determination and orientation recovery, is applied in many intelligent manufacturing fields. In this paper, six-degree-of-freedom (6DOF) pose measurement with dual-quaternion-introduced absolute orientation using a pseudo-multi-camera system is presented. By planning to straight move and rotate the single camera at a reasonable angle, a pseudo-multi-camera system with the required viewpoints and baseline distances is generated to dynamically build a measurement network. Based on multi-view localization beams from the measurement network, a stereo recovery method is proposed to allow three-dimensional (3D) extraction. Dual-quaternion-introduced absolute orientation for 6DOF estimation is studied to avoid translation extraction dependent on rotation and strict initial value selection. Considering the feature point information from different viewpoints, the 6DOF pose is further refined by multi-view bundle adjustment. The experiment results show that our method outperforms classical methods in accuracy and stability, especially in translation measurement. It also can arrange observation points as needed to adapt to complex measurement scenes. It is expected to be used in scenarios where traditional multi-camera setups are infeasible, such as pipeline inspection, gastrointestinal endoscopy, and shield machine guidance.

Designing minimal-layer antireflection coatings that achieve, for the coated substrate, an average transmittance above 99% over a broadband range remains a challenging task. In this study, we introduce for two- and three-layer configurations an exhaustive search method combined with a transmittance map representation. Such an approach enables, for the first time, to our knowledge, a clear visualization of all refractive index combinations yielding the highest mean transmittance values, thereby offering a comprehensive overview of the optimal designs.

This study presents an end-to-end laser stripe centerline extraction model, CSA-ASPP-Net, which enables direct mapping from raw laser stripe images to high-precision sub-pixel centerlines through the integrated design of atrous spatial pyramid pooling (ASPP) and channel-spatial attention (CBAM). Our model addresses the limitations of traditional methods, which rely heavily on manually designed features, as well as existing deep learning approaches, which require segmentation-extraction centerlines. By innovatively integrating attention-guided feature enhancement and multi-scale contextual perception modules into an encoder-decoder architecture, the proposed model enables single-stage completion of stripe localization and refinement. The experimental results demonstrate that this end-to-end framework achieves a precision of 94.85% and an average localization error of 0.64 pixels in test images, with a processing speed of 0.15 s per image, highlighting its computational efficiency. The results demonstrate that the CBAM module effectively mitigates background interference by emphasizing salient features, while the ASPP module enhances adaptability to various stripe morphologies through its multi-scale capability. This research provides an innovative and integrated solution tailored for structured light measurement systems, combining efficiency with high precision in laser stripe processing.

Multispectral illumination facilitates a broad spectrum of biomedical applications. However, there remains a scarcity of multispectral light sources with a multimode optical fiber output that are spectrally tunable and suitable for small-footprint applications. We present a comprehensive open-source guide for constructing a compact and adaptable fiber-coupled multispectral illumination platform featuring five LEDs in the 455 to 940 nm wavelength range. The key properties of the LED platform, including output power, spectral performance, and stability, are thoroughly characterized. This platform marks the first available multi-LED system, to our knowledge, capable of delivering optical output power in the milliwatt range when coupled to a multimode fiber. Blueprints, assembly instructions, parts lists, source code, and a graphical interface are provided in a Zenodo repository to promote accessibility and expand its use.

This paper presents a compact and high-efficiency solid-state three-dimensional (3D) light detection and ranging (LiDAR) system based on subpixel imaging and microlens array-based optical multiplexing. The proposed system integrates a two-dimensional laser diode array transmitter and a receiver composed of a projection lens, a microlens-based multiplexer, and a small-scale avalanche photodiode array. To overcome the limitations of traditional fiber bundle-based multiplexers, a dual-layer microlens array structure is developed, enabling precise subpixel focusing and enhanced spatial multiplexing. The system supports multiple encoding strategies-offering high-resolution imaging (640×480) when using less patterns with CMOS sensors (320×240) and fast 3D imaging when raising encoding counts for downscale detectors (2×2). Experimental results confirm the effectiveness of the design in both 3D point cloud reconstruction and 2D multiplexed imaging scenarios. The proposed architecture is scalable and suitable for integration with time-of-flight modules. This subpixel imaging technique can greatly reduce the number of detectors required in imaging LiDAR systems, making it suitable for low-cost LiDAR applications.

This paper proposes a novel infrared cone-shaped spatial target recognition method, to the best of our knowledge, based on an improved multiple kernel extreme learning machine (MKELM) for the problem that radiation intensity sequence is the only data type available and is often contaminated by noise at long-range detection. Variational mode decomposition (VMD) and reconstruction are incorporated for radiation intensity sequence. Then, the whale optimization algorithm (WOA) is used to optimize parameters, and target recognition is tested on a simulated infrared radiation intensity sequence dataset using improved MKELM. The experimental results verify the effectiveness of the method and show its enhanced recognition accuracy and robustness.

We propose an improved cross-correlation strain demodulation method of the optical frequency domain reflectometry (OFDR) to further enhance the measurement accuracy in dual-requirement scenarios of high spatial resolution and large dynamic strain measurement range. Rather than a conventional signal processing method based on one-dimensional (1D), we transform the distributed global spectrum shifts obtained through cross-correlation calculation along the sensing fiber as a two-dimensional image matrix and employ a denoising convolutional neural network to smoothen the systemic artifacts manifested as cross-correlation fake peaks due to the local similarity degradation between the obtained reference (Ref.) and measured (Mea.) spectra, thereby enabling the reconstruction of accurate strain gradient profiles. Experimental results reveal that this performance enhancement achieves a fivefold and twofold improvement, from 19.63% and 49.53% to 98.27%, respectively, in demodulation accuracy over the 1D moving average smoothing and 1D convolutional neural network methods under an employed strain of 300 µɛ and without hardware modifications. Meanwhile, the strain profiles across both zero-strain and stretched regions at a consistent spatial resolution of 16 mm are measured accurately and clearly. The measured strain linearly changes with the applied strain, with a slope and R² of 1.03 and 0.99, respectively, when the applied strain is increased from 100 to 900 µɛ, which confirms the efficacy of our proposed scheme in addressing traditional OFDR demodulation challenges.

The annual periodicity present in spectral remote sensing reflectances (R_rs(λ)) derived from ocean color satellite data processed by NASA's Ocean Biology Processing Group has been shown to diverge from that observed in situ, with the source of the discrepancies remaining unresolved. Here, we investigate such periodicity in reflectances derived from four satellite sensors and seven in situ locations. Periodicity in the time series of R_rs(λ) spectra is examined without prior assumption of the main constituent frequency through application of the floating-mean Lomb-Scargle periodogram. We show that a one-term sinusoidal function specified at the annual frequency with a constant offset reproduces seasonal trends observed at various regional oceanic sites. Although unique regional features in mean, amplitude, and phase are generally well-captured by R_rs(λ) derived from satellite measurements, discrepancies perpetuate the annual frequency into the satellite-to-in situR_rs(λ) matchup difference. Specifying the origin of the discrepancy is challenging because the annual frequency is significantly contained in time series of many physical measurements, corrections, and ancillary data required for R_rs(λ) retrievals. Until differences in amplitude and phase between satellite and in situR_rs(λ) time series are reduced, improved uncertainty estimates should be incorporated into downstream product analyses.

This study investigates the broadband absorption of electromagnetic (EM) waves in temporal metamaterials (TEMLs). These materials are temporal multilayer structures with a time-varying refractive index, n(t), and have attracted significant attention in recent years. While considerable progress has been made in TEML research, the broadband absorption phenomenon still requires further investigation. The transmission matrix method (TMM) was used to analyze the transmission characteristics of EM waves in TEMLs, focusing on the broadband absorption phenomenon. This broadband absorption is based on the frequency conversion properties of the temporal interface. Due to the isotropic nature of TEMLs, their absorption characteristics remain consistent across all incident angles, demonstrating complete polarization insensitivity. The unique temporal properties of TEMLs overcome the limitations of time-harmonic materials and enable the tunability of EM properties. This study advances the technology for controlling EM waves in the time domain and offers new, to our knowledge, strategies for designing novel, to the best of our knowledge, devices.

High-voltage power cables are essential for long-distance energy transmission, where insulation reliability is especially critical at joints and terminations. Polymeric materials, such as silicone rubber (SiR), are widely used, but they remain vulnerable to internal defects, such as air voids and moisture, which can degrade performance and cause failure. Conventional non-destructive evaluation methods, including ultrasonic testing, X-ray imaging, and partial discharge measurements, have limitations in resolution, safety, or field applicability. Terahertz (THz) imaging has emerged as a promising alternative, offering non-contact, high-resolution inspection with sensitivity to moisture. Previous THz studies on polymeric insulators have demonstrated detection of 100 µm scale defects in either the depth or lateral direction, while the defect size along the other dimension typically remained in the millimeter range. To the best of our knowledge, the simultaneous detection of subsurface defects on the order of 100 µm in both directions within polymeric insulators has not yet been achieved. This study demonstrates the feasibility of visualizing internal defects within bulk SiR insulation using a solid immersion lens-based THz time-domain spectroscopy system. Void defects as small as 100 µm in both depth and lateral directions were clearly resolved near the surface. In addition, void defects with a thickness of 100 µm and lateral diameters of several hundred micrometers were detectable at depths up to 1 mm. Furthermore, a layered defect comprising a void and a metallic inclusion was imaged, revealing the inclusion clearly resolved beyond the void, a result unattainable using ultrasonic inspection due to high reflectivity at air interfaces.