Robust and adaptive star identification algorithm based on linear assignment for multiple large field of view visual imaging systems.

The integration of the visual imaging system and the self-attitude determination system in on-orbit space projects necessitates robust star identification algorithms. However, disturbances in the on-orbit environment pose a challenge to the accuracy and efficiency of star identification algorithms. This paper introduces a novel star identification algorithm, to the best of our knowledge, designed for multiple large field of view (FOV) visual imaging systems, providing stability in the presence of the noise types, including position noise, lost-star noise, and fake-star noise. We employ the dynamic simulated star images generation method to construct simulated star image libraries suitable for various cameras with different FOV angles. Our algorithm comprises two parts: the star edge matching for coarse matching of interstellar angular distances based on linear assignment, and the star point registration for precise matching of star vectors. This innovative combination of local edge generation and global matching approach achieves an impressive 97.83% identification accuracy, maintaining this performance even with a standard deviation of one pixel in image plane errors and up to five missing stars. A comprehensive approach involving both simulated and empirical experiments was employed to validate the algorithm's effectiveness. This integration of the visual imaging system and the self-attitude determination system offers potential benefits such as reduced space equipment weight, simplified satellite launch processes, and decreased maintenance costs.

Camera optimal projection model identification and calibration method based on the NGO-BA architecture.

In order to bridge the fundamental commonalities between imaging models of camera lenses with different principles and structures, allowing for an accurate description of imaging characteristics across a wide range of field-of-view (FOV), we have proposed a generic camera calibration method on the basis of the projection model optimization strategy. First, in order to cover the current mainstream projection models, piecewise functions for geometric projection models and a polynomial function for the fitting projection model are designed. Then, the corresponding camera multistation self-calibration bundle adjustment (BA) module is developed for various projection models. Further, by integrating the self-calibration BA algorithm into the northern goshawk optimization architecture, iterative optimization is performed on the projection model adjustment parameters, camera interior parameters, camera exterior parameters, and lens distortion parameters until the target reprojection (RP) error reaches the global minimum. The experimental results indicate that the calibration RP root mean square error in this method is 1/20 pixel for a 68° FOV camera, 1/13 pixel for an 84° FOV camera, 1/9 pixel for a 115° FOV camera, 1/9 pixel for a 135° FOV camera, and 1/6 pixel for a 180° FOV camera. This calibration method offers fast and versatile optimization for various projection model types, encompassing a wide range of projection functions. It can efficiently determine the optimal projection model and all imaging parameters for multiple cameras during the calibration process.

ECFuse: Edge-Consistent and Correlation-Driven Fusion Framework for Infrared and Visible Image Fusion.

Infrared and visible image fusion (IVIF) aims to render fused images that maintain the merits of both modalities. To tackle the challenge in fusing cross-modality information and avoiding texture loss in IVIF, we propose a novel edge-consistent and correlation-driven fusion framework (ECFuse). This framework leverages our proposed edge-consistency fusion module to maintain rich and coherent edges and textures, simultaneously introducing a correlation-driven deep learning network to fuse the cross-modality global features and modality-specific local features. Firstly, the framework employs a multi-scale transformation (MST) to decompose the source images into base and detail layers. Then, the edge-consistent fusion module fuses detail layers while maintaining the coherence of edges through consistency verification. A correlation-driven fusion network is proposed to fuse the base layers containing both modalities' main features in the transformation domain. Finally, the final fused spatial image is reconstructed by inverse MST. We conducted experiments to compare our ECFuse with both conventional and deep leaning approaches on TNO, LLVIP and M3FD datasets. The qualitative and quantitative evaluation results demonstrate the effectiveness of our framework. We also show that ECFuse can boost the performance in downstream infrared-visible object detection in a unified benchmark.

Resonant-cavity-enhanced infrared detector incorporating an ultra-thin type-II superlattice: design and simulation.

A resonant-cavity-enhanced type-II superlattice (T2SL) infrared detector based on a metal grating has been designed to address the weak photon capture and low quantum efficiency (QE) issues of T2SL infrared detectors. Simulations have been conducted to analyze the effects of metal grating parameters, including length, thickness, and incident angle, on the spectral response and absorptivity of the absorption layers in T2SL infrared detectors. By optimizing the design, an appropriate resonant cavity structure was obtained. Research results indicate that the resonant cavity structure can significantly enhance the absorption rate of a T2SL infrared detector with a 0.2 µm thick absorption layer in the 3-5 µm wavelength range, observing peak absorption rates at 3.82 µm and 4.73 µm, with values of 97.6% and 98.2%, respectively. The absorption rate of the 0.2 µm thick T2SL absorption layer at peak wavelengths increased from 6.03% and 2.3% to 54.48% and 27.91%, respectively. The implementation of the resonant-cavity-enhanced T2SL infrared detector improves the QE while reducing absorption layer thickness, thus opening up new avenues for improving T2SL detector performance.

All-parameter calibration method of the on-orbit multi-view dynamic photogrammetry system.

Photogrammetry (PG) can present accurate data to evaluate the functional performance of large space structures. For camera calibration and orientation, the On-orbit Multi-view Dynamic Photogrammetry System (OMDPS) lacks appropriate spatial reference data. A multi-data fusion calibration method for all parameters for this kind of system is proposed in this paper as a solution to this issue. Firstly, a multi-camera relative position model is developed to solve the reference camera position unconstrained problem in the full-parameter calibration model of the OMDPS in accordance with the imaging model of stars and scale bar targets. Subsequently, the problem of adjustment failure and inaccurate adjustment in the multi-data fusion bundle adjustment is solved using the two-norm matrix and the weight matrix to adjust the Jacobian matrix with respect to all system parameters (e.g., camera interior parameters (CIP), camera exterior parameters (CEP), and lens distortion parameters (LDP)). Finally, all system parameters can be optimized simultaneously using this algorithm. In the actual data ground-based experiment, 333 spatial targets are measured using the V-star System (VS) and OMDPS. Taking the measurement of VS as the true value, the measurement results of OMDPS indicated that the in-plane Z-direction target coordinates root-mean-square error (RMSE) is less than 0.0538 mm and the Z-direction RMSE is less than 0.0428 mm. Out-of-plane Y-direction RMSE is less than 0.1514 mm. The application potential of the PG system for on-orbit measurement tasks is demonstrated through the actual data ground-based experiment.

PLC-Based Arrayed Waveguide Grating Design for Fiber Bragg Grating Interrogation System.

A fiber Bragg grating (FBG) interrogator is a scientific instrument that converts the wavelength change of FBG sensors into readable electrical signals. To achieve miniaturization and integration of FBG interrogator, we designed and fabricated a 36-channel array waveguide grating (AWG) on silica-based planar lightwave circuits (PLC) as a key device in a built FBG interrogation system. It is used to achieve continuous demodulation in C-band, while maintaining high resolution. This AWG has a 1.6 nm channel spacing, 3-dB bandwidth of 1.76 nm, non-adjacent channel crosstalk of -29.76 dB, and insertion loss of 3.46 dB. The dynamic range of the FBG interrogation system we built was tested to be 1522.4-1578.4 nm, with an interrogation resolution of 1 pm and accuracy of less than 1 pm in the dynamic range of 1523.16-1523.2 nm. The test results show that the FBG interrogation technology, based on AWG, can realize FBG wavelengths accurately demodulated, which has high application value in aerospace, deep sea exploration, and environmental monitoring, as well as other fields.

Optical fiber shape sensing of polyimide skin for a flexible morphing wing: retraction.

The referenced article [Appl. Opt.56, 9325 (2017)APOPAI0003-693510.1364/AO.56.009325] has been retracted by the authors.

Modeling and simulation of all-optical diffractive neural network based on nonlinear optical materials.

In this Letter, we propose an all-optical diffractive deep neural network modeling method based on nonlinear optical materials. First, the nonlinear optical properties of graphene and zinc selenide (ZnSe) are analyzed. Then the optical limiting effect function corresponding to the saturation absorption coefficient of the nonlinear optical materials is fitted. The optical limiting effect function is taken as the nonlinear activation function of the neural network. Finally, the all-optical diffractive neural network model based on nonlinear materials is established. The numerical simulation results show that the model can effectively improve the nonlinear representation ability of the all-optical diffractive neural network. It provides a theoretical support for the further realization of a photonic artificial intelligence chip based on nonlinear optical materials.

A metal-organic zeolitic framework with immobilized urease for use in a tapered optical fiber urea biosensor.

A tapered single-mode coreless single-mode (SCS) structure with high sensitivity for sensing refractive index is described. In order to achieve high specificity of optical biosensors, here enzyme capsulation film was achieved by embedding urease in zeolitic imidazolate framework (ZIF-8/urease) through in situ growth approach on the coreless fibers. Determination of urea is achieved through online monitoring of its binding to the urease in zeolitic imidazolate framework. Refractive index change result in wavelength shifts of the optical fiber biosensor. The resonance wavelength exhibits a good linear relationship with urea concentration in the range of 1 to 10 mM with detection limit of 0.1 mM and sensitivity of 0.8 mM/RIU (refractive index unit) if operated with broadband light ranging from 1525 nm to 1590 nm. Final assessment of optical biosensor in real sample was performed where excellent performance in terms of sensitivity and selectivity was observed. Schematic representation of experimental setup and mechanism for urea detection. A tapered single-mode coreless single-mode (SCS) structure is placed between a broadband light source ranging (BBS) and optical spectrum analyzer (OSA). ZIF-8/urease composites are applied as a recognition layer for urea detection.

Long non-coding RNA MIR205HG regulates KRT17 and tumor processes in cervical cancer via interaction with SRSF1.

Abnormal expression of long non-coding RNAs (lncRNAs) has been demonstrated to be a vital regulatory factor in a large number of malignancies. The investigation in cervical cancer and the associated modulation mechanisms are yet to be probed. The aim of this study is to specifically investigate the expression pattern and modulatory mechanism of MIR205HG in cervical cancer. Our paper firstly revealed the up-regulation of KRT17 in cervical cancer. Function assays further displayed that KRT17 silencing impaired the proliferation and migration, and activated the apoptosis of cervical cancer cells. Based on the finding that MIR205HG could regulate KRT17 expression, we further probed the detailed mechanism between MIR205HG and KRT17. It was observed from mechanism experiments that MIR205HG depleted SRSF1 to increase KRT17 expression. The whole mechanism of MIR205HG/SRSF1/KRT17 axis affecting cell proliferation, apoptosis and migration in cervical cancer was validated using rescue assays. In conclusion, MIR205HG modulated the biological activities of cervical cancer cells via targeting SRSF1 and regulating KRT17, which better understood the pathogenesis of cervical carcinoma and excavated a novel therapeutic target.

A Random-displacement Measurement by Combining a Magnetic Scale and Two Fiber Bragg Gratings.

Long-distance displacement measurements using optical fibers have always been a challenge in both basic research and industrial production. We developed and characterized a temperature-independent fiber Bragg grating (FBG)-based random-displacement sensor that adopts a magnetic scale as a novel transferring mechanism. By detecting shifts of two FBG center wavelengths, a full-range measurement can be obtained with a magnetic scale. For identification of the clockwise and counterclockwise rotation direction of the motor (in fact, the direction of movement of the object to be tested), there is a sinusoidal relationship between the displacement and the center wavelength shift of the FBG; as the anticlockwise rotation alternates, the center wavelength shift of the second FBG detector shows a leading phase difference of around 90° (+90°). As the clockwise rotation alternates, the center wavelength shift of the second FBG displays a lagging phase difference of around 90° (-90°). At the same time, the two FBG-based sensors are temperature independent. If there is some need for a remote monitor without any electromagnetic interference, this striking approach makes them a useful tool for determining the random displacement. This methodology is appropriate for industrial production. As the structure of the whole system is relatively simple, this displacement sensor can be used in commercial production. In addition to it being a displacement sensor, it can be used to measure other parameters, such as velocity and acceleration.

Correction: Remarkable electronic and optical anisotropy of layered 1T'-WTe2 2D materials.

[This corrects the article DOI: 10.3762/bjnano.10.170.].

Remarkable electronic and optical anisotropy of layered 1T'-WTe2 2D materials.

Anisotropic 2D materials exhibit novel optical, electrical and thermoelectric properties that open possibilities for a great variety of angle-dependent devices. Recently, quantitative research on 1T'-WTe2 has been reported, revealing its fascinating physical properties such as non-saturating magnetoresistance, highly anisotropic crystalline structure and anisotropic optical/electrical response. Especially for its anisotropic properties, surging research interest devoted solely to understanding its structural and optical properties has been undertaken. Here we report quantitative, comprehensive work on the highly anisotropic, optical, electrical and optoelectronic properties of few-layer 1T'-WTe2 by azimuth-dependent reflectance difference microscopy, DC conductance measurements, as well as polarization-resolved and wavelength-dependent optoelectrical measurements. The electrical conductance anisotropic ratio is found to ≈103 for a thin 1T'-WTe2 film, while the optoelectronic anisotropic ratio is around 300 for this material. The polarization dependence of the photo-response is ascribed to the unique anisotropic in-plane crystal structure, consistent with the optical absorption anisotropy results. In general, 1T'-WTe2, with its highly anisotropic electrical and photoresponsivity reported here, demonstrates a route to exploit the intrinsic anisotropy of 2D materials and the possibility to open up new ways for applications of 2D materials for light polarization detection.

Microdeformation of RBCs under oxidative stress measured by digital holographic microscopy and optical tweezers.

This paper utilized digital holographic microscopy and optical tweezers to study microdeformation of red blood cells (RBCs) dynamically under oxidative stress. RBCs attached with microbeads were stretched by dual optical tweezers to generate microdeformation. Morphology of RBCs under manipulation were recorded dynamically and recovered by off-axis digital holographic microscopy method. RBCs treated with H2O2 at different concentrations were measured to investigate the mechanical properties under oxidative stress. Use of optical tweezers and off-axis digital holographic microscopy enhanced measuring accuracy compared with the traditional method. Microdeformation of RBCs is also more consistent with the physiological situation. This proposal is meaningful for clinical applications and basic analysis of Parkinson's disease research.

Deep convolutional neural networks for tongue squamous cell carcinoma classification using Raman spectroscopy.

With deep convolutional neural networks and fiber optic Raman spectroscopy, this study presents a novel classification method that discriminates tongue squamous cell carcinoma (TSCC) from non-tumorous tissue. To achieve this purpose, 24 tissues spectral data were first collected from 12 patients who had undergone a surgical resection due to the tongue squamous cell carcinomas. Then 6 blocks with each block including 1 convolutional layer and 1 max-pooling layer are used to extract the nonlinear feature representations from Raman spectra. The derived features form a representative vector, which is fed into a fully-connected network for performing classification task. Experimental results demonstrated that the proposed method achieved high sensitivity (99.31%) and specificity (94.44%). To show the superiority for the ConvNets classifier, comparison results with the state-of-the-art methods show it had a competitive classification accuracy. Moreover, these promising results may pave the way to apply the deep ConvNets model in the fiber optic Raman instrument for intra-operative evaluation of TSCC resection margins and improve patient survival.

Characterization of metallic-packaging fiber Bragg grating sensors with coated and bare fibers.

Metallic packaging of fiber Bragg grating (FBG) sensors is developed using the ultrasonic welding method. Both polyimide-coated fiber and bare fiber could be bonded well to aluminum alloy substrate using Sn-Bi alloy. Two kinds of metal-packaged FBG sensors, coated FBG and bare FBG, are characterized for studying the thermal sensitivity, strain response, short-term creep, and temporal temperature response. Both FBG sensors showed increasing sensitivity with temperature from -40°C to 80°C. The metal-packaged coated FBG sensor displayed relative strengths in strain stability, repeatability, creep, spectra shape, and temperature response when compared with the bare one. Moreover, the boundaries between optical fiber and metal alloy are intact, and cross-sectional scanning electron microscope micrographs clearly illustrated that metal alloy coated well with the coated and bare fiber.

Optical Fiber Sensor Performance Evaluation in Soft Polyimide Film with Different Thickness Ratios.

To meet the application requirements of curvature measurement for soft biomedical robotics and flexible morphing wings of aircraft, the optical fiber Bragg grating (FBG) shape sensor for soft robots and flexible morphing wing was implemented. This optical FBG is embedded in polyimide film and then fixed in the body of a soft robot and morphing wing. However, a lack of analysis on the embedded depth of FBG sensors in polyimide film and its sensitivity greatly limits their application potential. Herein, the relationship between the embedded depth of the FBG sensor in polyimide film and its sensitivity and stability are investigated. The sensing principle and structural design of the FBG sensor embedded in polyimide film are introduced; the bending curvatures of the FBG sensor and its wavelength shift in polyimide film are studied; and the relationship between the sensitivity, stability, and embedded depth of these sensors are verified experimentally. The results showed that wavelength shift and curvature have a linear relationship. With the sensor's curvature ranging from 0 m-1 to 30 m-1, their maximum sensitivity is 50.65 pm/m-1, and their minimum sensitivity is 1.96 pm/m-1. The designed FBG sensor embedded in polyimide films shows good consistency in repeated experiments for soft actuator and morphing wing measurement; the FBG sensing method therefore has potential for real applications in shape monitoring in the fields of soft robotics and the flexible morphing wings of aircraft.

Simultaneous Robot⁻World and Hand⁻Eye Calibration without a Calibration Object.

An extended robot⁻world and hand⁻eye calibration method is proposed in this paper to evaluate the transformation relationship between the camera and robot device. This approach could be performed for mobile or medical robotics applications, where precise, expensive, or unsterile calibration objects, or enough movement space, cannot be made available at the work site. Firstly, a mathematical model is established to formulate the robot-gripper-to-camera rigid transformation and robot-base-to-world rigid transformation using the Kronecker product. Subsequently, a sparse bundle adjustment is introduced for the optimization of robot⁻world and hand⁻eye calibration, as well as reconstruction results. Finally, a validation experiment including two kinds of real data sets is designed to demonstrate the effectiveness and accuracy of the proposed approach. The translation relative error of rigid transformation is less than 8/10,000 by a Denso robot in a movement range of 1.3 m × 1.3 m × 1.2 m. The distance measurement mean error after three-dimensional reconstruction is 0.13 mm.

Simultaneous All-Parameters Calibration and Assessment of a Stereo Camera Pair Using a Scale Bar.

Highly accurate and easy-to-operate calibration (to determine the interior and distortion parameters) and orientation (to determine the exterior parameters) methods for cameras in large volume is a very important topic for expanding the application scope of 3D vision and photogrammetry techniques. This paper proposes a method for simultaneously calibrating, orienting and assessing multi-camera 3D measurement systems in large measurement volume scenarios. The primary idea is building 3D point and length arrays by moving a scale bar in the measurement volume and then conducting a self-calibrating bundle adjustment that involves all the image points and lengths of both cameras. Relative exterior parameters between the camera pair are estimated by the five point relative orientation method. The interior, distortion parameters of each camera and the relative exterior parameters are optimized through bundle adjustment of the network geometry that is strengthened through applying the distance constraints. This method provides both internal precision and external accuracy assessment of the calibration performance. Simulations and real data experiments are designed and conducted to validate the effectivity of the method and analyze its performance under different network geometries. The RMSE of length measurement is less than 0.25 mm and the relative precision is higher than 1/25,000 for a two camera system calibrated by the proposed method in a volume of 12 m × 8 m × 4 m. Compared with the state-of-the-art point array self-calibrating bundle adjustment method, the proposed method is easier to operate and can significantly reduce systematic errors caused by wrong scaling.