Step-adaptive accelerated demodulation algorithm for LFM-pulse-based distributed acoustic sensing.

We propose a novel, to our knowledge, step-adaptive cross-correlation algorithm tailored for distributed acoustic sensing systems based on linear frequency modulation pulses, aiming for rapid demodulation. This algorithm adjusts its step length through an adaptive "successive refinement" search strategy, which greatly improves computational efficiency by reducing the number of cross-correlation computations. Experimental results have shown that the demodulation time can be reduced by approximately 15 times compared to the conventional method, while maintaining the same demodulation result.

Autonomous Microlasers for Profiling Extracellular Vesicles from Cancer Spheroids.

Self-propelled micro/nanomotors are emergent intelligent sensors for analyzing extracellular biomarkers in circulating biological fluids. Conventional luminescent motors are often masked by a highly dynamic and scattered environment, creating challenges to characterize biomarkers or subtle binding dynamics. Here we introduce a strategy to amplify subtle signals by coupling strong light-matter interactions on micromotors. A smart whispering-gallery-mode microlaser that can self-propel and analyze extracellular biomarkers is demonstrated through a liquid crystal microdroplet. Lasing spectral responses induced by cavity energy transfer were employed to reflect the abundance of protein biomarkers, generating exclusive molecular labels for cellular profiling of exosomes derived from 3D multicellular cancer spheroids. Finally, a microfluidic biosystem with different tumor-derived exosomes was employed to elaborate its sensing capability in complex environments. The proposed autonomous microlaser exhibits a promising method for both fundamental biological science and applications in drug screening, phenotyping, and organ-on-chip applications.

Polarized image super-resolution via a deep convolutional neural network.

Reduced resolution of polarized images makes it difficult to distinguish detailed polarization information and limits the ability to identify small targets and weak signals. A possible way to handle this problem is the polarization super-resolution (SR), which aims to obtain a high-resolution polarized image from a low-resolution one. However, compared with the traditional intensity-mode image SR, the polarization SR is more challenging because more channels and their nonlinear cross-links need to be considered as well as the polarization and intensity information need to be reconstructed simultaneously. This paper analyzes the polarized image degradation and proposes a deep convolutional neural network for polarization SR reconstruction based on two degradation models. The network structure and the well-designed loss function have been verified to effectively balance the restoration of intensity and polarization information, and can realize the SR with a maximum scaling factor of four. Experimental results show that the proposed method outperforms other SR methods in terms of both quantitative evaluation and visual effect evaluation for two degradation models with different scaling factors.

Curved fiber compound eye camera inspired by the Strepsiptera vision.

The Strepsiptera vision possesses intriguing features of a large field of view (FOV) and relatively high resolution compared to normal compound eyes. However, it presents a significant challenge of the mismatch between the curved compound eyelet lens array and the planar image sensor to image in a large FOV for artificial compound eyes (ACE). We propose what we believe to be a novel curved fiber compound eye camera (CFCEC) here, which employs coherent fiber bundles as the optical relay system to transmit sub-images curvilinearly. A total of 106 eyelets are arranged based on a scheme similar to the Goldberg polyhedron, with the advantages of uniform interval and minor edge blindness. Then, a prototype of the CFCEC is fabricated and assembled. A series of experiments are conducted to assess the FOV, contrast, resolution, and overlap rate of FOV of the prototype. The results prove that the CFCEC has a total FOV of up to 160°×160° and a total overlap rate of FOV of approximately 65%, demonstrating the promising potential of the CFCEC in various applications, such as panoramic surveillance, 3D detection, and motion tracking.

High sensitivity composite F-P cavity fiber optic sensor based on MEMS for temperature and salinity measurement of seawater.

We proposed an optical fiber salinity sensor with a composite Fabry-Perot (F-P) cavity structure for simultaneous measurement of temperature and salinity based on microelectromechanical system (MEMS) technology. The sensor contains two sensing cavities. The silicon cavity is used for temperature sensing, and the seawater cavity processed by the glass microstructure is sensitive to the refractive index of seawater for salinity sensing. At the same time, the influence of the salinity-temperature cross-sensitivity error of the seawater cavity is effectively compensated by using the temperature single parameter sensitivity characteristics of the silicon cavity. The structural design of the sensor seawater cavity includes a cross-shaped groove and a cylindrical fluid cavity. The surface hydrophilicity treatment was performed on the interior of the cavity to solve the effect of no water injection in the cavity caused by the miniaturization of the sensor. The optical path difference (OPD) demodulation method is used to demodulate the two F-P cavities with large dynamic range and high resolution. In the range of 5∼40°C and 5∼ 40 ‰, the temperature and salinity sensitivity of the sensor can reach 110.25 nm/°C and 178.75 nm/‰, respectively, and the resolution can reach 5.02 × 10-3°C and 0.0138‰. It has the advantages of mass production, high stability, and small size, which give it great potential for marine applications.

Simultaneous bond-selective deuterium-based isotopic labeling sensing with disposable ultra-miniature CARS fiber probe.

Deuterium-based isotopic labeling is an important technique for tracking cellular metabolism with the Raman signals analysis of low-wavenumber (LW) C-D bonds and high-wavenumber (HW) C-H bonds. We propose and demonstrate a disposable ultra-miniature fiber probe to detect LW and HW coherent anti-Stokes Raman scattering (CARS) spectra for deuterated compounds simultaneously and bond-selectively sensing. The 10.78 µm diameter disposable fiber probe, comprised of focusing taper as fiber probe head and time-domain walk-off eliminating fiber section with designed length, realizes wide-frequency-interval dual Stokes pulse delivering and focusing. The fiber probe enables quantitative concentration determination with resolution down to 11 mM. The chemical vibration modes of LW region C-D bonds and HW region C-H bonds of the mixture samples of organic compounds and their deuterated counterparts in a simulated cell are simultaneously excited and characterized. The CARS disposable fiber probe introduces a promising handle for in vivo biochemical detection based on isotopic labeling sensing.

Pol2Pol: self-supervised polarimetric image denoising.

In this Letter, we present a self-supervised method, polarization to polarization (Pol2Pol), for polarimetric image denoising with only one-shot noisy images. First, a polarization generator is proposed to generate training image pairs, which are synthesized from one-shot noisy images by exploiting polarization relationships. Second, the Pol2Pol method is extensible and compatible, and any network that performs well in supervised image denoising tasks can be deployed to Pol2Pol after proper modifications. Experimental results show Pol2Pol outperforms other self-supervised methods and achieves comparable performance to supervised methods.

High Sensitivity Temperature Sensing of Long-Period Fiber Grating for the Ocean.

In this study, a new temperature sensor with high sensitivity was achieved by four-layer Ge and B co-doped long-period fiber grating (LPFG) based on the mode coupling principle. By analyzing the mode conversion, the influence of the surrounding refractive index (SRI), the thickness and the refractive index of the film on the sensitivity of the sensor is studied. When 10 nm-thick titanium dioxide (TiO2) film is coated on the surface of the bare LPFG, the refractive index sensitivity of the sensor can be initially improved. Packaging PC452 UV-curable adhesive with a high-thermoluminescence coefficient for temperature sensitization can realize high-sensitivity temperature sensing and meet the requirements of ocean temperature detection. Finally, the effects of salt and protein attachment on the sensitivity are analyzed, which provides a reference for the subsequent application. The sensitivity of 3.8 nm/°C in the range of 5-30 °C was achieved for this new sensor, and the resolution is about 0.00026 °C, which is over 20 times higher than ordinary temperature sensors. This new sensor meets the accuracy and range of general ocean temperature measurements and could be used in various marine monitoring and environmental protection applications.

Analytical optimization of wideband nonlinear optical fiber communication systems.

In the design of fiber links for both continental and transoceanic optical communication systems, the optimization of span length is of high importance from both performance and cost perspectives. In this work, the maximization of signal-to-noise ratio (SNR) is investigated by optimizing the span length in wideband (up to 4.5-THz) Nyquist-spaced optical fiber communication systems. A simple and accurate closed-form expression of the optimal span length is provided, and a quick estimation of SNR is also described for practically feasible and cost-effective span length values.

Physics-informed neural network for polarimetric underwater imaging.

Utilizing the polarization analysis in underwater imaging can effectively suppress the scattered light and help to restore target signals in turbid water. Neural network-based solutions can also boost the performance of polarimetric underwater imaging, while most of the existing networks are pure data driven which suffer from ignoring the physical mode. In this paper, we proposed an effective solution that informed the polarimetric physical model and constrains into the well-designed deep neural network. Especially compared with the conventional underwater imaging model, we mathematically transformed the two polarization-dependent parameters to a single parameter, making it easier for the network to converge to a better level. In addition, a polarization perceptual loss is designed and applied to the network to make full use of polarization information on the feature level rather than on the pixel level. Accordingly, the network was able to learn the polarization modulated parameter and to obtain clear de-scattered images. The experimental results verified that the combination of polarization model and neural network was beneficial to improve the image quality and outperformed other existing methods, even in a high turbidity condition.

Automatic bifurcation detection utilizing pullback characteristics of bifurcation in intravascular optical coherence tomography.

Bifurcation detection in coronary arteries is significant since it influences the treatment strategy selection and optimization. Bifurcations are also reliable landmarks for image registration. Intravascular optical coherence tomography (IVOCT) is a high-resolution imaging modality that is very useful in percutaneous coronary intervention stenting optimization. We present a bifurcation identification method utilizing pullback characteristics for IVOCT, which can effectively identify the bifurcations with a small size. The longitudinal view of the pullback will appear as an outward discontinuity in the bifurcation area. By detecting this discontinuity, bifurcation can be identified with high accuracy. We also use the normal vectors method to extract the ostium of bifurcation. We compare the proposed method with the widely-used distance transformation method by clinical 5302 IVOCT images from 22 pullbacks. The average metrics of true positive rate (TPR), true negative rate (TNR), positive predictive value (PPV), and negative predictive value (NPV) for the proposed method are 86.97%, 98.50%, 85.56%, and 98.67%, respectively. TPR, PPV, and NPV by the proposed method are improved by 40.24%, 9.31%, 3.90%, and TNR is on par compared with the distance transformation method. Especially in the small bifurcation identification, TPR of the proposed method is 64.71% higher than the distance transformation method with a bifurcation area ratio less than 0.2.

High-sensitive MEMS Fabry-Perot pressure sensor employing an internal-external cavity Vernier effect.

In this paper, a high sensitivity pressure sensor employing an internal-external cavity Vernier effect is innovatively achieved with the microelectromechanical systems (MEMS) Fabry-Perot (FP) interferometer. The sensor consists of silicon cavity, vacuum cavity, and silicon-vacuum hybrid cavity, which is fabricated by direct bonding a silicon diaphragm with an etched cylindrical cavity and a silicon substrate. By rationally designing the optical lengths of the silicon cavity and silicon-vacuum hybrid cavity to match, the internal-external cavity Vernier effect will be generated. The proposed cascaded MEMS FP structure exhibits a pressure sensitivity of -1.028 nm/kPa by tracking the envelope evolution of the reflection spectrum, which is 58 times that of the silicon-vacuum hybrid cavity. What's more, it owns a minimal temperature sensitivity of 0.041 nm/°C for the envelope spectrum. The MEMS FP sensor based on internal-external cavity Vernier effect as the promising candidate provides an essential guideline for high sensitivity pressure measurement under the characteristic of short FP sensing cavity length, which demonstrates the value to the research community.

Analysis and reduction of noise-induced depolarization in catheter based polarization sensitive optical coherence tomography.

In catheter based polarization sensitive optical coherence tomography (PS-OCT), a optical fiber with a rapid rotation in the catheter can cause low signal-to-noise ratio (SNR), polarization state instability, phase change of PS-OCT signals and then heavy noise-induced depolarization, which has a strong impact on the phase retardation measurement of the sample. In this paper, we analyze the noise-induced depolarization and find that the effect of depolarization can be reduced by polar decomposition after incoherent averaging in the Mueller matrix averaging (MMA) method. Namely, MMA can reduce impact of noise on phase retardation mapping. We present a Monte Carlo method based on PS-OCT to numerically describe noise-induced depolarization effect and contrast phase retardation imaging results by MMA and Jones matrix averaging (JMA) methods. The peak signal to noise ratio (PSNR) of simulated images processed by MMA is higher than about 8.9 dB than that processed by JMA. We also implement experiments of multiple biological tissues using the catheter based PS-OCT system. From the simulation and experimental results, we find the polarization contrasts processed by the MMA are better than those by JMA, especially at areas with high depolarization, because the MMA can reduce effect of noise-induced depolarization on the phase retardation measurement.

Reconstruction error model of distributed shape sensing based on the reentered frame in OFDR.

At present, the reconstruction error of optical fiber shape sensing is commonly represented by Euclidean distance error. However, the Euclidian error of shape reconstruction will be dependent on the shape complexity, which depends on length, curvature and torsion. In this paper, we establish a reconstruction error model of distributed shape sensing in optical frequency domain reflectometry (OFDR) based on the Frenet-Serret frame and the error delivering theory, which illustrates the relationship between the reconstruction error and parameters such as curvature, torsion, fiber length and strain measurement error. We experimentally verify the feasibility and applicability of the proposed reconstruction error model by distributed optical fiber shape sensing system based on OFDR. The proposed reconstruction error model can provide a prediction of the maximal reconstruction error when the estimated range of curvature, torsion, fiber length of a shape needs to be reconstructed and strain measurement errors of OFDR system are known. It is very useful to judge whether the shape reconstruction error meets the requirement according to the shape to be reconstructed.

Ultra-thin mid-infrared silicon grating coupler.

Mid-infrared (mid-IR) silicon photonics has been attracting great attention due to its tremendous potential applications in nonlinear optics, ranging, sensing, and spectroscopy. To date, mid-IR silicon devices have usually been developed based on silicon wafers with top-layer silicon thicknesses of hundreds of nanometers. Compared with the thick silicon devices, tens-of-nanometers thin silicon devices can provide giant evanescent-field energy proportions and optical mode areas, being significant for many biochemical sensing and nonlinear optics applications. However, ultra-thin mid-IR silicon devices have seldom been studied due to the difficulty of light coupling. Here, we demonstrated an ultra-thin focusing subwavelength-grating coupler for mid-IR ultra-thin suspended subwavelength-grating-cladding waveguide coupling. The results show that the grating has a maximum coupling efficiency of -7.1 dB at a center wavelength of 2200 nm with a 1-dB bandwidth of ∼115 nm and back reflection of -19.9 dB. We also measured the fiber alignment tolerance of 12 µm for 3-dB coupling efficiency reduction and bending optical loss of 0.25 dB/90°. Our results pave the way to developing mid-IR ultra-thin photonic integrated circuits.

Mid-infrared polarization-insensitive grating coupler.

Mid-infrared (Mid-IR) (2-20 µm) silicon photonics has attracted much attention in the past few years due to its application potential in free-space optical communications, light detection and ranging, and molecular analysis. The grating coupler technology is one of the most widely employed approaches for light coupling between optical fibers and waveguides. In the mid-IR spectral region, due to the lack of reliable chalcogenide-fiber or ZBLAN-fiber polarization controllers, grating couplers usually suffer from huge insertion losses induced by the arbitrary polarization states of light coupled out of mid-IR fibers. As a result, it is significant to explore polarization-insensitive grating coupling techniques in mid-IR wavelengths. However, the study is currently still in its infancy. Here, we demonstrate an ultra-thin mid-IR polarization-insensitive grating coupler. The grating coupler has a maximum coupling efficiency of -11.5 dB at a center wavelength of ∼2200 nm with a 1-dB bandwidth of ∼148 nm. Compared with conventional subwavelength grating couplers, the polarization-dependent loss was improved from 9.6 dB to 2.1 dB. Moreover, we demonstrated a polarization-insensitive grating coupler at 2700-nm wavelength with a maximum coupling efficiency of -12.0 dB. Our results pave the way for the development of mid-IR photonic integrated circuits.

Intensity self-compensation method against multi-factors for polarization-based Fabry-Perot interrogation system.

Phase interrogation methods for fiber-optic Fabry-Perot (F-P) sensors may inevitably fail in the field due to the influences of irrelevant factors on signal intensity. To address this severe problem, this Letter proposes an intensity self-compensation method (ISCM) to eliminate the consecutive signal fluctuations of a polarization-based F-P interrogation system caused by multiple factors. By providing only the initial intensities of the reference signals, this attempt realizes the real-time intensity compensation of the output signals without affecting their quadrature relationship. Consecutive intensity fluctuations caused by variation of light source power, fiber loss, and polarization state are reduced to 2%-3% by the ISCM. Furthermore, the method performs ideally under dynamic modulation of the sensor. In addition, it can be applied against the inconsistent fluctuations between signals and is suitable for F-P sensors with single or multiple cavities. Owing to the high efficiency, real-time ability, and no moving parts advantage, the proposed method provides an excellent candidate for improving the accuracy and stability of F-P interrogation systems.

Improved spectral demodulation algorithm with EEMDAD for sapphire fiber Fabry-Perot high-temperature sensing.

In this paper, an improved spectral demodulation algorithm with the ensemble empirical mode decomposition average denoising is proposed to suppress order jumps in the sapphire fiber Fabry-Perot high-temperature sensing system. It is proven that the signal-to-noise ratio of the sensor is closely related to the severity of the demodulation jumps. The proposed algorithm can reduce the fluctuations of key parameters by reducing the noise in the spectrum, thus overcoming this obstacle. The simulations and experiment demonstrate that the algorithm can effectively eliminate the order jumps in both stable and variable temperature environments. The proposed algorithm solves the order jumping problem that has long plagued the demodulation of this system, improves demodulation accuracy, ensures the reliable operation of the high-temperature sensor, and exhibits excellent demodulation performance.

A Demodulation Algorithm for Periodically In-Plane Vibrating MEMS Based on a Stroboscopic Micro-Visual System.

A high-frequency short-pulsed stroboscopic micro-visual system was employed to capture the transient image sequences of a periodically in-plane working micro-electro-mechanical system (MEMS) devices. To demodulate the motion parameters of the devices from the images, we developed the feature point matching (FPM) algorithm based on Speeded-Up Robust Features (SURF). A MEMS gyroscope, vibrating at a frequency of 8.189 kHz, was used as a testing sample to evaluate the performance of the proposed algorithm. Within the same processing time, the SURF-based FPM method demodulated the velocity of the in-plane motion with a precision of 10−5 pixels of the image, which was two orders of magnitude higher than the template-matching and frame-difference algorithms.

Ratiometric Optical Fiber Dissolved Oxygen Sensor Based on Fluorescence Quenching Principle.

In this study, a ratiometric optical fiber dissolved oxygen sensor based on dynamic quenching of fluorescence from a ruthenium complex is reported. Tris(4,7-diphenyl-1,10-phenanthrolin) ruthenium(II) dichloride complex (Ru(dpp)32+) is used as an oxygen-sensitive dye, and semiconductor nanomaterial CdSe/ZnS quantum dots (QDs) are used as a reference dye by mixing the two substances and coating it on the plastic optical fiber end to form a composite sensitive film. The linear relationship between the relative fluorescence intensity of the ruthenium complex and the oxygen concentration is described using the Stern-Volmer equation, and the ruthenium complex doping concentration in the sol-gel film is tuned. The sensor is tested in gaseous oxygen and aqueous solution. The experimental results indicate that the measurement of dissolved oxygen has a lower sensitivity in an aqueous environment than in a gaseous environment. This is due to the uneven distribution of oxygen in aqueous solution and the low solubility of oxygen in water, which results in a small contact area between the ruthenium complex and oxygen in solution, leading to a less-severe fluorescence quenching effect than that in gaseous oxygen. In detecting dissolved oxygen, the sensor has a good linear Stern-Volmer calibration plot from 0 to 18.25 mg/L, the linearity can reach 99.62%, and the sensitivity can reach 0.0310/[O2] unit. The salinity stability, repeatability, and temperature characteristics of the sensor are characterized. The dissolved oxygen sensor investigated in this research could be used in various marine monitoring and environmental protection applications.