Traditional Optical Method Research Articles

Continuous Viscoelasticity Measurement of Cell Spheroids via Microfluidic Electrical Aspiration.

Measurement of viscoelastic characteristics of cells cultured in three-dimensional (3D) is critical to study many biological processes including tissue and organ growth. In this article, we present a unique electrical aspiration method to measure the viscoelastic properties of cell spheroids. A microfluidic sensor was created to demonstrate this method. Unlike the traditional optical aspiration method, the aspiration of the cell spheroids is monitored via monitoring the dynamic electrical resistance change of a symmetrical microfluidic aspiration channel. We first used the microfluidic device to measure the viscoelastic properties of two types of biological tissues derived from calfskin and porcine left ventricular myocardium. The equilibrium elastic modulus and creep time constants were measured to be 148.1 ± 24.1 kPa and 76.7 ± 3.5 s and 64.5 ± 7.7 kPa and 31.4 ± 2.7 s respectively, which matched well with reported data. The test validated the principle of the electrical aspiration method. Next, we applied the method for measuring cell spheroids made of human mesenchymal stem cells at different culture stages. The equilibrium elastic modulus and apparent viscosity decreased with increasing culture time. Compared to optical aspiration methods, this microfluidic electrical aspiration method has no reliance on transparent materials and image processing, which thus allows simple setup, fast data acquisition and analysis. The use of a symmetric aspiration channel and the linear half-space model enable measurements of a large number of viscoelastic properties via a single measurement with higher accuracy. This method will enable high throughput, continuous viscoelastic measurement of cell spheroids as well as other 3D cell culture models in flow conditions without the need for any optical measurements.

Photopolymerized 3D Printing Materials for Optical Elements

AbstractThe optical element is a key part of manufacturing optoelectronic systems, which can realize functions such as imaging, spectroscopy, image transmission, filtering, and more. The design and manufacture of optical elements is a key issue that restricts its development, and the traditional optical component preparation method is less capable of preparing optical elements with complex structures and multiple materials. As a mature advanced manufacturing technology, photopolymerization 3D printing technology has the advantages of high printing precision and small layer thickness, which plays an important role in the preparation of optical elements. This review paper briefly provides a concise overview of the benefits and applications of photopolymerization 3D printing technology in optical element preparation The development overview and research progress of materials used for the preparation of optical elements by photopolymerization 3D printing are discussed. Finally, it summarizes the current limitations, challenges, future development prospects, and potential applications in the realm of optical element manufacturing.

Simplified design method for optical imaging systems based on aberration characteristics of optical-digital joint optimization.

With the continuous improvement of imaging performance requirements, the design of imaging systems has become increasingly complex, making it more difficult and expensive to manufacture and test. To overcome these problems, a simplified design framework for imaging systems based on aberration characteristics of optical-digital joint optimization was built in this paper. Specifically, an improved total variation regularization restoration algorithm was proposed, and the difficulty of correction for different monochromatic aberrations was evaluated. With this evaluation, the proposed algorithm was combined with the traditional optical design method to jointly correct the aberration and simplify the optical system by relaxing the requirements for optical structures and surface shapes under the guarantee of the imaging performance. To demonstrate the feasibility and efficiency of the method, three design examples are provided, where the structure similarity index measure of the simulation imaging results is on the same level as that of the initial system, with a maximum error not exceeding 0.04. The simulation results demonstrate that our design method can effectively simplify the optical structure of imaging systems while maintaining high performance.

Lightweight Video-Based Respiration Rate Detection Algorithm: An Application Case on Intensive Care

The video-based non-contact respiration detection technology can be used in many application scenarios to unobtrusively and ubiquitously monitor the physical state of living beings, and various researchers are currently working on this technology. The optical flow method in tandem with crossover point method is rather effective for respiration rate extraction. However, each method has one disadvantage: 1) the redundant feature points in the traditional optical flow method increase the computational effort and reduce the estimation accuracy; and 2) the traditional crossover point method suffers from crossover points unrelated to breathing movements. For these two challenges, two optimization points are proposed in this work: 1) optimize feature point space by combining spatio-temporal information; and 2) use negative feedback design to adaptively remove crossovers that are not related to respiratory movements. The performance of the proposed algorithm is validated by the Large-scale Bedside Respiration Dataset for Intensive Care (LBRD-IC), which is established using the actual surveillance videos acquired from ICU wards. The validity of the above two optimization points is verified by the ablation experiments. The influential analysis of computation time and video resolution on the performance of the proposed algorithm demonstrates that the proposed algorithm can be deployed to various application terminals to monitor the respiration rate of living organisms in real-time and with high accuracy. In addition, field measurements in the ICU ward have shown that our algorithm can measure respiratory signals of the single patient and multiple patients when only one surveillance camera is present. The dataset and the code are publicly available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ShawnTan86/LBRD-IC-Dataset</uri> .

Identification of dust particles on a periodic nanostructured substrate using scanning electron microscope imaging

Dust-mitigating surfaces typically consist of high-aspect-ratio structures that separate particles from resting on the bulk material, thereby limiting adhesion due to short-range van der Waals forces. These surfaces can find uses in solar-panel coatings and a variety of dust-resistant optics. The current method for quantifying surface contamination is optical microscopy, but this method is inadequate for observing particles at the submicrometer scale due to the diffraction limit. Furthermore, regardless of the microscopy technique, particle identification becomes problematic as the particle contaminates approach the same length scale of the surface structures. In this work, we demonstrate a method to identify micro-/nanoparticle contaminates on nanostructured surfaces using electron microscopy and image processing. This approach allows the characterization of particles that approach the length scale of the surface structures. Image processing, including spectrum filters and edge detection, is used to remove the periodic features of the surface nanostructure to omit them from the particle counting. The detection of these small particles using electron microscopy leads to an average of 5.62 particles/100 μm2 detected compared to 0.63 particles/100 μm2 detected for the traditional confocal optical detection method. Beyond dust-mitigation nanostructures, the demonstrated particle detection technique can find applications in nanobiology, the detection of ice nucleation on a structured surface, and semiconductor mask inspections.

Machine Learning for Fog-and-Low-Stratus Nowcasting from Meteosat SEVIRI Satellite Images

Fog and low stratus (FLS) are meteorological phenomena that have a significant impact on all ways of transportation and public safety. Due to their similarity, they are often grouped together as a single category when viewed from a satellite perspective. The early detection of these phenomena is crucial to reduce the negative effects that they can cause. This paper presents an image-based approach for the short-term nighttime forecasting of FLS during the next 5 h over Morocco, based on geostationary satellite observations (Meteosat SEVIRI). To achieve this, a dataset of hourly night microphysics RGB product was generated from native files covering the nighttime cold season (October to April) of the 5-year period (2016–2020). Two optical flow techniques (sparse and dense) and three deep learning techniques (CNN, Unet and ConvLSTM) were used, and the performance of the developed models was assessed using mean squared error (MSE) and structural similarity index measure (SSIM) metrics. Hourly observations from Meteorological Aviation Routine Weather Reports (METAR) over Morocco were used to qualitatively compare the FLS existence in METAR, where it is also shown by the RGB product. Results analysis show that deep learning techniques outperform the traditional optical flow method with SSIM and MSE of about 0.6 and 0.3, respectively. Deep learning techniques show promising results during the first three hours. However, their performance is highly dependent on the number of filters and the computing resources, while sparse optical flow is found to be very sensitive to mask definition on the target phenomenon.

Deep Learning Optical Flow with Compound Loss for Dense Fluid Motion Estimation

Flow motion with complex patterns, such as vortex, stagnant flow, and seepage, put forward higher spatial resolution requirements for particle image velocimetry (PIV). With the development of deep learning technology in optical flow estimation, many attempts have been made to introduce deep learning-based optical flow (DLOF) into PIV. Compared with the traditional optical flow method, the DLOF method has the advantages of higher precision, faster calculation speed, and avoiding manual parameter adjustment. However, DLOF research is generally developed based on the basic characteristics of rigid body motion, and its key loss function part still generally uses the L1 (mean absolute error, MAE, L1) or L2 (mean square error, MSE, L2) loss functions, which lack consideration of fluid motion characteristics. Therefore, the current DLOF research has the problems of large angular error and serious curl-divergence loss in fluid motion estimation scenarios with smaller spatial scales than rigid bodies. Based on the prior knowledge of the traditional fluid motion characteristics, this study proposes a fluid loss function for describing the fluid motion characteristics, and combines this loss function with Flownet2. The compound loss (CL) function is combined with the displacement error, angular error, and div-curl smooth loss. The method combined with the loss function in this paper is called FlowNet2-CL-PIV. In order to verify that the compound loss function proposed in this study has a positive impact on the model training results, this paper uses the cosine similarity measure to demonstrate its effectiveness. In addition, the parameter selection of the compound loss function is analyzed and compared, and it is verified that the best training effect can be achieved by adjusting the parameter so that the order of magnitude of each part of the compound loss function is consistent. In order to test the calculation effect of the Flownet2-CL-PIV method proposed in this study, synthetic particle images are used for model training and performance analysis. Simulation results in various flow fields show that the root mean square error (RMSE) and average angular error (AAE) of Flownet2-CL-PIV reach 0.182 pixels and 1.7°, which are 10% and 54% higher than the original model, respectively.

MOP-SLAM: A real time SLAM system based on multi-head optical flow estimation network

SLAM (simultaneous localization and mapping) is becoming a significant technology in driverless vehicles nowadays. Sometimes the optical flow method can be used to track feature points to reduce the amount of calculation. But the traditional optical flow method is based on the assumption that the luminosity will not change and has high requirements for the environment. Therefore, an optical flow calculation method based on neural network called Optical-Net is proposed in this paper to match and track feature and corner points. We use the multi-head attention module to extract and fuse the features of different scales. The training results show that the Optical-Net designed in this study effectively improves the robustness and accuracy of optical flow estimation when the object has large displacement and small displacement. Meanwhile, to address the problem of reduced matching accuracy during video input due to dynamic blurring, a video dynamic deblurring algorithm based on the one-dimensional Wineman filter is adopted to preprocess the input video. Finally, we replaced the tracking thread in traditional SLAM. The performance of the improved algorithm is verified by experiments on our own driverless car.

Characterization of high-speed electro-optic phase modulators based on heterodyne carrier mapping at a fixed low-frequency.

A self-referenced method based on heterodyne carrier mapping is proposed to characterize the modulation efficiency of high-speed electro-optic phase modulators (EOPMs). The heterodyne carrier mapping replicates the optical carrier after phase modulation to an electrical replica, which enables observing the power variation of the optical carrier at a fixed low-frequency in the electrical domain. The modulation depths and half-wave voltages within the frequency range of up to 40 GHz are determined by measuring the amplitude ratio of the mapped low-frequency component at 80 MHz in the cases of on and off single-tone modulation of the EOPM. The measured results are compared to those obtained with the traditional optical spectrum analysis method and the electrical spectrum sweep method to check the consistency and accuracy. Surpassing the heterodyne spectrum mapping (HSM) scheme, our method only requires a single-tone driving of the EOPM under test and completely avoids the roll-off responsivity of the photodetector through the fixed low-frequency detection.

Weather Radar Nowcasting for Extreme Precipitation Prediction Based on the Temporal and Spatial Generative Adversarial Network

Since strong convective weather is closely related to heavy precipitation, the nowcasting of convective weather, especially the nowcasting based on weather radar data, plays an essential role in meteorological operations for disaster prevention and mitigation. The traditional optical flow method and cross-correlation method have a low forecast accuracy and a short forecast leading time, while deep learning methods show remarkable advantages in nowcasting. However, most of the current forecasting methods based on deep learning suffer from the drawback that the forecast results become increasingly blurred as the forecast time increases. In this study, a weather radar nowcasting method based on the Temporal and Spatial Generative Adversarial Network (TSGAN) is proposed, which can obtain accurate forecast results, especially in terms of spatial details, by extracting spatial-temporal features, combining attention mechanisms and using a dual-scale generator and a multi-scale discriminator. The case studies on the forecast results of strong convective weather demonstrate that the GAN method performs well in terms of forecast accuracy and spatial detail representation compared with traditional optical flow methods and popular deep learning methods. Therefore, the GAN method proposed in this study can provide strong decision support for forecasting heavy precipitation processes. At present, the proposed method has been successfully applied to the actual weather forecasting business system.

Laser ultrasonic loading and optical interference detection of closed cracks in K9 glass.

The traditional optical detection method has a limitation in detecting closed cracks in optical elements. In this Letter, a novel, to the best of our knowledge, method has been developed for the detection of closed cracks in transparent samples. Firstly, behaviors of closed cracks under ultrasonic waves loading are demonstrated through the finite-element method (FEM). Simulation results show that the crack can be opened by the laser ultrasonic shear wave (S-wave). Then, a Mach-Zehnder interferometer system is experimentally established to detect closed cracks under ultrasonic waves loading in the sample. It is demonstrated that closed cracks which cannot be detected by interferometry become visible in interferogram images under ultrasonic waves loading. The width of the opened crack can also be quantitatively estimated. Thus the method would be a promising approach to detect initially closed cracks in optical elements.

Design and evaluation of a dual-beam read/write configuration for optical storage

Increasing the number of read/write layers and ensuring the quality of read/write signals are the key factors to enhance the optical storage capacity. However, limited by the single optical path, the structure, and the material of the disk, the existing system reads and writes up to 16 layers. In the existing optical storage system design, the optical path design and the signal model are independent of each other, so the signal quality based on the signal model can only be evaluated after the implementation of the optical path. We propose a dual-beam read/write optical path for the optical storage system, which breaks through the limitation of the single optical path and can read/write multilayers. The servo detection signals and servo margins show that the dual-beam read/write system has good error detection capability and strong robustness. The servo error signal is calculated according to the relationship between the wavefront aberration, relative coordinate system displacement difference, and servo error of the optical system, thus deriving a quantifiable signal evaluation model. The effectiveness of the model is verified by experiments. The signal evaluation model breaks through the limitations of the traditional optical design method for the optical storage system and can guide the optical design from the signal perspective. The dual-beam optical path for multilayer optical storage systems provides a broader range of ideas for enhancing optical storage capacity.

Study on cage slip for rolling bearing under speed variation conditions

The rolling bearing failure from cage slip under fast speed variation is an important issue that has drawn a wide interest. To ensure the safe operation, cage slip needs to be detected. However, due to the limit of bearing working environment of high temperature, oil mist, and small mounting space, the conventional methods such as optical fiber, ultrasound, and eddy current detection are not always feasible. This paper presents a novel method to detect cage slip based on weak magnetic detection under the speed variation. The rolling bearing, which is in the earth magnetic field, can be magnetized weakly. By the detection of the weak magnetized bearing on the earth magnetic field disturbance, the roller speed information can be obtained. This method is not affected by the bearing work environment. Then, the signal processing technology based on the ridge detection is proposed. By the speed spectrum of inner raceway, the searched path can be re-grouped. And the searched boundary is determined by assuming the roller pure rolling. Combining the searched path and the boundary, the local neighborhood of the roller rotational instantaneous frequency (IF) is determined. By finding the magnitude maximum of the local neighborhood, the roller IF can be extracted. Finally, the time-varying spectrum of the cage slip can be evaluated under the speed variation. By the comparison with the traditional optical method, it illustrates that the cage slip goes through dramatic changes under speed variation and is almost unchanged under the stabilization stage.

A Preliminary Study on the Evaluation of Human Sperm Head Morphology with a Domestic Digital Holographic Image System.

The head of sperm was imaged with domestic digital holographic microscopy (DHM), and then the quantitative three-dimensional size information of normal sperm and teratozoospermic sperm was compared and analyzed. DHM sperm imaging and repeated quantitative evaluation were used to determine the morphology of the sperm head in two patients with teratozoospermia and four volunteers with normal semen parameters. Sixty and 139 sperm of teratozoospermia patients and normal people were photographed by digital hologram, respectively. The differences in head height and width were compared and statistically analyzed. The sperm head height of the teratozoospermia group was 3.06 ± 1.66μm, which was significantly lower than that of the normal sperm group (4.54 ± 1.60μm, p < 0.01), but there was no significant difference in the head width between the two groups. Compared with the traditional two-dimensional optical microscope observation method, the DHM system can provide three-dimensional quantitative information for the sperm head and thus may help in the comprehensive clinical evaluation of the sperm head structure.

Characterizing microring resonators using optical frequency domain reflectometry.

A novel, to the best of our knowledge, method to extract optical microring resonators' loss characteristics is proposed and demonstrated using optical frequency domain reflectometry (OFDR). Compared with the traditional optical transmission measurement method, the spatial-resolved backscattering optical signals obtained from the OFDR can clearly show the resonance mode's increased optical path length due to its circulation inside the resonator. By further processing the backscattered optical signals, loaded $Q$-factors of several resonators can be accurately determined. A calculation model is proposed to derive the resonance mode's intrinsic $Q$-factor from OFDR measurements of a series of loaded resonators.

Study on Local Optical Flow Method Based on YOLOv3 in Human Behavior Recognition

In the process of human behavior recognition, the traditional dense optical flow method has too many pixels and too much overhead, which limits the running speed. This paper proposed a method combing YOLOv3 (You Only Look Once v3) and local optical flow method. Based on the dense optical flow method, the optical flow modulus of the area where the human target is detected is calculated to reduce the amount of computation and save the cost in terms of time. And then, a threshold value is set to complete the human behavior identification. Through design algorithm, experimental verification and other steps, the walking, running and falling state of human body in real life indoor sports video was identified. Experimental results show that this algorithm is more advantageous for jogging behavior recognition.

Image tracking and matching algorithm of semi-dense optical flow method

The traditional optical flow method is based on the assumption of spatial consistency of the optical flow field. It is easy to reduce the tracking quality and even lead to the loss of target tracking in the areas of image feature missing, boundary and occlusion. A semi-dense optical flow method is proposed to realise stable tracking of image features. Firstly, the feature points are preserved by calculating the pixel points with large change of pixel gradient in the image; Secondly, according to the principle of grey level invariance, the grey level difference function between the corresponding feature points of adjacent frames is constructed; Finally, the gradient descent principle is used to optimise the grey difference function and realise the accurate matching of feature points of adjacent frames. The results show that compared with the traditional LK optical flow method, this algorithm can effectively improve the feature tracking capability, and at the same time can effectively retain the useful information in the image. Compared with the traditional feature point matching method, the algorithm presented in this paper has an efficient operation rate.

A Precipitation Nowcasting Mechanism for Real-World Data Based on Machine Learning

Unpredicted precipitations, even mild, may cause severe economic losses to many businesses. Precipitation nowcasting is hence significant for people to make correct decisions timely. For traditional methods, such as numerical weather prediction (NWP), the accuracy is limited because the smaller scale of strong convective weather must be smaller than the minimum scale that the model can capture. And it often requires a supercomputer. Furthermore, the optical flow method has been proved to be available for precipitation nowcasting. However, it is difficult to determine the model parameters because the two steps of tracking and extrapolation are separate. In contrast, current machine learning applications are based on well-selected full datasets, ignoring the fact that real datasets quite often contain missing data requiring extra consideration. In this paper, we used a real Hubei dataset in which a few radar echo data are missing and proposed a proper mechanism to deal with the situation. Furthermore, we proposed a novel mechanism for radar reflectivity data with single altitudes or cumulative altitudes using machine learning techniques. From the experimental results, we conclude that our method can predict future precipitation with a high accuracy when a few data are missing, and it outperforms the traditional optical flow method. In addition, our model can be used for various types of radar data with a type-specific feature extraction, which makes the method more flexible and suitable for most situations.

Learnable Optical Flow Network for Radar Echo Extrapolation

The impact of extreme weather on maintaining flight schedules is becoming more pronounced. Currently, radar echo extrapolation technology is widely used in the nowcasting of severe convection, in which the optical flow method is a representative example of traditional extrapolation algorithms. By training a large number of known samples to find the optimal solution, the deep extrapolation models have gradually become better than the traditional algorithms in recent years. In this study, after examining the optical flow method and other deep learning models, a learnable optical flow deep model with a fully convolutional structure is proposed. Using the convolutional deep learning of optical flow information, this new model can overcome the kernel size limitation of traditional convolutional neural networks, and it can correlate the data history further in time and space. The six-year radar mosaics of Guangdong Province, China, were used as the data set to independently train and verify the new model. The results reveal that the new model outperformed the traditional optical flow method and it is also better than other deep learning models.

Single-shot digital phase-shifting Moiré patterns for 3D topography.

A simple and robust technique of Moiré topography with single-image capture and incorporating digital filtering along with a four-step digitally implemented phase-shifting method is introduced for three-dimensional (3D) surface mapping. Feature details in the order of tens to hundreds of microns can be achieved using interferometrically generated structured light to illuminate the object surface. Compared to the traditional optical phase-shifting method, a digital phase-shifting method based on Fourier processing is implemented with computer-generated sinusoidal patterns derived from the recorded deformed fringes. This enables a single capture of the image that can be used to reconstruct the 3D topography of the surface. Single-shot imaging is simple to implement experimentally and avoids errors in introducing the correct phase shifts. The feasibility of this technique is verified experimentally, and applications to metallic surfaces are demonstrated.