A Segmented Cross-Correlation Algorithm for Dynamic North Finding Using Fiber Optic Gyroscopes.

Fiber optic gyroscope (FOG)-based north finding is extensively applied in navigation, positioning, and various fields. In dynamic north finding, an accelerated turntable speed shortens the time required for north finding, resulting in a rapid north-finding response. However, with an increase in turntable speed, the turntable's jitter contributes to signal contamination in the FOG, leading to a deterioration in north-finding accuracy. This paper introduces a divide-and-conquer algorithm, the segmented cross-correlation algorithm, designed to mitigate the impact of turntable speed jitter. A model for north-finding error is established and analyzed, incorporating FOG's self-noise and the turntable's speed jitter. To validate the feasibility of our method, we implemented the algorithm on a FOG. The simulation and experimental results exhibited a strong concordance, affirming the validity of our proposed north-finding error model. The experimental findings indicate that, at a turntable speed of 180°/s, the north-finding bias error within a 360 s duration is 0.052°, representing a 64% improvement over the traditional algorithm. These results indicate the effectiveness of the proposed algorithm in mitigating the impact of unstable turntable speeds, offering a solution for north finding with both prompt response and enhanced accuracy.

Video-rate 3D imaging of living cells using Fourier view-channel-depth light field microscopy.

Interrogation of subcellular biological dynamics occurring in a living cell often requires noninvasive imaging of the fragile cell with high spatiotemporal resolution across all three dimensions. It thereby poses big challenges to modern fluorescence microscopy implementations because the limited photon budget in a live-cell imaging task makes the achievable performance of conventional microscopy approaches compromise between their spatial resolution, volumetric imaging speed, and phototoxicity. Here, we incorporate a two-stage view-channel-depth (VCD) deep-learning reconstruction strategy with a Fourier light-field microscope based on diffractive optical element to realize fast 3D super-resolution reconstructions of intracellular dynamics from single diffraction-limited 2D light-filed measurements. This VCD-enabled Fourier light-filed imaging approach (F-VCD), achieves video-rate (50 volumes per second) 3D imaging of intracellular dynamics at a high spatiotemporal resolution of ~180 nm × 180 nm × 400 nm and strong noise-resistant capability, with which light field images with a signal-to-noise ratio (SNR) down to -1.62 dB could be well reconstructed. With this approach, we successfully demonstrate the 4D imaging of intracellular organelle dynamics, e.g., mitochondria fission and fusion, with ~5000 times of observation.

Interferometric fiber-optic gyroscope based on mode-division multiplexing.

The interferometric fiber-optic gyroscope (IFOG) is widely used in the fields of inertial navigation and rotational seismology. A direct way to improve the sensitivity of the IFOG is to increase the length of the sensing fiber, but this increases the cost and size of the gyroscope. Here, we propose an IFOG based on mode-division multiplexing (MDM), which exhibits relatively high performance. The experimental results show that, the proposed IFOG is improved to twice as much in terms of sensitivity, angle random walk, and bias instability with the use of MDM. This research provides a novel, to the best of our knowledge, solution for the design and implementation of low-cost, high-sensitivity IFOGs, which could contribute to their application in a wider range of fields.

Dual-polarization interferometric fiber optic gyroscope based on a four-port circulator.

The dual-polarization interferometric fiber optic gyroscope (IFOG) has been studied for many years and achieved remarkable performance. In this study, we propose a novel dual-polarization IFOG configuration based on a four-port circulator, in which the polarization coupling errors and the excess relative intensity noise are well handled meanwhile. Experimental measurements of the short-term sensitivity and long-term drift using a fiber coil with a length of 2 km and a diameter of 14 cm show that the angle random walk of 5.0×10-5∘/h and bias instability of 9.0 × 10-5 °/h are achieved. Moreover, the root power spectrum density of 20n r a d/s/H z is almost flat from 0.001 Hz to 30 Hz. We believe this dual-polarization IFOG is a preferred candidate for the reference-grade performance IFOG.

Compensation of scale factor nonlinear error introduced by harmonic distortion for open-loop fiber optic gyroscopes.

The scale factor (SF) of a gyroscope is the ratio of the detection output rotational rate and the input, and is expected to be a constant. However, for open-loop interferometric fiber optic gyroscopes (IFOGs) with sinusoidal modulation, harmonic amplitudes are inevitably affected by detection defects, such as nonuniform frequency response of the photodetector or unequal gain of amplification circuits. As a result, harmonic distortion leads to SF nonlinearity, which seriously hinders the accuracy of high-precision gyroscopes. In this Letter, the theoretical form of the SF error introduced by harmonic distortion of open-loop gyroscopes is analyzed, and an effective and simple compensation method is proposed. Instead of traversing the whole dynamic range, the proposed method simplifies the calibration pretest, where only a section of the dynamic range needs to be tested. Experimental results on an open-loop IFOG prototype show that, with our proposed method, the SF nonlinear error is suppressed to 2.5 ppm within the range -300 to +300∘/s, which is 33 times less than that before compensation.

A practical guide to deep-learning light-field microscopy for 3D imaging of biological dynamics.

Here, we present a step-by-step protocol for the implementation of deep-learning-enhanced light-field microscopy enabling 3D imaging of instantaneous biological processes. We first provide the instructions to build a light-field microscope (LFM) capable of capturing optically encoded dynamic signals. Then, we detail the data processing and model training of a view-channel-depth (VCD) neural network, which enables instant 3D image reconstruction from a single 2D light-field snapshot. Finally, we describe VCD-LFM imaging of several model organisms and demonstrate image-based quantitative studies on neural activities and cardio-hemodynamics. For complete details on the use and execution of this protocol, please refer to Wang et al. (2021).1.

Angular accelerometer based on a dual-polarization fiber-optic Sagnac interferometer.

High-performance angular accelerometers are essential for precise dynamics control of aircraft, satellites, etc. Here, we propose, for the first time to the best of our knowledge, an angular accelerometer based on a dual-polarization fiber-optic Sagnac interferometer, which exhibits relatively high sensitivity and a broad bandwidth. The experimental results show that the angular accelerometer achieves a flat frequency response in the bandwidth range of 0.01-100 Hz. The sensitivity reaches 6.6 × 10-8 rad/s2/Hz. In addition, the proposed fiber-optic angular accelerometer does not rely on any mechanical structure and has strong environmental adaptability. This research provides a feasible solution for the design and implementation of new high-performance angular accelerometers, which contributes to their development in the fields of inertial navigation and rotational seismology.

Optical projection tomography reconstruction with few views using highly-generalizable deep learning at sinogram domain.

Optical projection tomography (OPT) reconstruction using a minimal number of measured views offers the potential to significantly reduce excitation dosage and greatly enhance temporal resolution in biomedical imaging. However, traditional algorithms for tomographic reconstruction exhibit severe quality degradation, e.g., presence of streak artifacts, when the number of views is reduced. In this study, we introduce a novel domain evaluation method which can evaluate the domain complexity, and thereby validate that the sinogram domain exhibits lower complexity as compared to the conventional spatial domain. Then we achieve robust deep-learning-based reconstruction with a feedback-based data initialization method at sinogram domain, which shows strong generalization ability that notably improves the overall performance for OPT image reconstruction. This learning-based approach, termed SinNet, enables 4-view OPT reconstructions of diverse biological samples showing robust generalization ability. It surpasses the conventional OPT reconstruction approaches in terms of peak-signal-to-noise ratio (PSNR) and structural similarity (SSIM) metrics, showing its potential for the augment of widely-used OPT techniques.

Identification of the major photodegradant in metronidazole by LC-PDA-MS and its reveal in compendial methods.

Metronidazole in aqueous solution is sensitive to light and UV irradiation, leading to the formation of N-(2-hydroxyethyl)-5-methyl-l,2,4-oxadiazole-3-carboxamide. This is revealed here by liquid chromatography with tandem photo diode array detection and mass spectrometry (LC-PDA-MS) and further verified by comparison with the corresponding reference substance and proton nuclear magnetic resonance (1H-NMR). However, in current compendial tests for related substances/organic impurities of metronidazole, the above photolytic degradant could not be detected. Thus, when photodegradation of metronidazole occurs, it could not be demonstrated. In our study, an improved LC method was developed and validated, which includes a detection at a wavelength of 230 nm and optimization of mobile phase composition thereby a better separation was obtained.

The Development of a New IFOG-Based 3C Rotational Seismometer.

For many years, seismological research mainly focuses on translational ground motions due to the lack of appropriate sensors. However, because of the development of devices based on Sagnac effect, measuring rotational waves directly comes available. In this work, a portable three-component broadband rotational seismometer named RotSensor3C based on open loop interferometric fiber optic gyroscope (IFOG) is designed and demonstrated. Laboratory tests and results are illustrated in detail. The self-noise ranging from 0.005 Hz to 125 Hz is about 1.2×10-7rads-1/Hz, and with the harmonics compensation the scale factor variation over ±250∘/s is lower than 10 ppm (parts per million). The misalignment matrix method is adopted to revise the output rotation rate. In a special near field experiment using the explosive source, the back-azimuths and phase velocity are estimated by the recorded acceleration and rotation rate. All the results prove the practicability of this new rotational sensor.

Real-time volumetric reconstruction of biological dynamics with light-field microscopy and deep learning.

Light-field microscopy has emerged as a technique of choice for high-speed volumetric imaging of fast biological processes. However, artifacts, nonuniform resolution and a slow reconstruction speed have limited its full capabilities for in toto extraction of dynamic spatiotemporal patterns in samples. Here, we combined a view-channel-depth (VCD) neural network with light-field microscopy to mitigate these limitations, yielding artifact-free three-dimensional image sequences with uniform spatial resolution and high-video-rate reconstruction throughput. We imaged neuronal activities across moving Caenorhabditis elegans and blood flow in a beating zebrafish heart at single-cell resolution with volumetric imaging rates up to 200 Hz.

Deep learning-enabled efficient image restoration for 3D microscopy of turbid biological specimens.

Though three-dimensional (3D) fluorescence microscopy has been an essential tool for modern life science research, the light scattering by biological specimens fundamentally prevents its more widespread applications in live imaging. We hereby report a deep-learning approach, termed ScatNet, that enables reversion of 3D fluorescence microscopy from high-resolution targets to low-quality, light-scattered measurements, thereby allowing restoration for a blurred and light-scattered 3D image of deep tissue. Our approach can computationally extend the imaging depth for current 3D fluorescence microscopes, without the addition of complicated optics. Combining ScatNet approach with cutting-edge light-sheet fluorescence microscopy (LSFM), we demonstrate the image restoration of cell nuclei in the deep layer of live Drosophilamelanogaster embryos at single-cell resolution. Applying our approach to two-photon excitation microscopy, we could improve the signal-to-noise ratio (SNR) and resolution of neurons in mouse brain beyond the photon ballistic region.

Deep-learning super-resolution light-sheet add-on microscopy (Deep-SLAM) for easy isotropic volumetric imaging of large biological specimens.

Isotropic 3D histological imaging of large biological specimens is highly desired but remains highly challenging to current fluorescence microscopy technique. Here we present a new method, termed deep-learning super-resolution light-sheet add-on microscopy (Deep-SLAM), to enable fast, isotropic light-sheet fluorescence imaging on a conventional wide-field microscope. After integrating a minimized add-on device that transforms an inverted microscope into a 3D light-sheet microscope, we further integrate a deep neural network (DNN) procedure to quickly restore the ambiguous z-reconstructed planes that suffer from still insufficient axial resolution of light-sheet illumination, thereby achieving isotropic 3D imaging of thick biological specimens at single-cell resolution. We apply this easy and cost-effective Deep-SLAM approach to the anatomical imaging of single neurons in a meso-scale mouse brain, demonstrating its potential for readily converting commonly-used commercialized 2D microscopes to high-throughput 3D imaging, which is previously exclusive for high-end microscopy implementations.