Experimental study on the variation of optical remote sensing imaging characteristics of internal solitary waves with wind speed

Optical remote sensing has been widely used to study internal solitary waves(ISWs).Wind speed has an important effect on ISW imaging of optical remote sensing.The light and dark bands of ISWs cannot be observed by optical remote sensing when the wind is too strong.The relationship between the characteristics of ISWs bands in optical remote sensing images and the wind speed is still unclear.The influence of wind speeds on the characteristics of the ISWs bands is investigated based on the physical simulation experiments with the wind speeds of 1.6,3.1,3.5,3.8,and 3.9 m/s.The experimental results show that when the wind speed is 3.9 m/s,the ISWs bands cannot be observed in optical remote sensing images with the stratification of h_(1)∶h_(2)=7∶58,ρ_(1)∶ρ_(2)=1∶1.04.When the wind speeds are 3.1,3.5,and 3.8 m/s,which is lower than 3.9 m/s,the ISWs bands can be obtained in the simulated optical remote sensing image.The location of the band’s dark and light extremum and the band’s peak-to-peak spacing are almost not affected by wind speed.More-significant wind speeds can cause a greater gray difference of the light-dark bands.This provided a scientific basis for further understanding of ISW optical remote sensing imaging.

High-throughput phenotyping of plant leaf morphological, physiological,and biochemical traits on multiple scales using optical sensing

Acquisition of plant phenotypic information facilitates plant breeding, sheds light on gene action, and can be applied to optimize the quality of agricultural and forestry products. Because leaves often show the fastest responses to external environmental stimuli, leaf phenotypic traits are indicators of plant growth,health, and stress levels. Combination of new imaging sensors, image processing, and data analytics permits measurement over the full life span of plants at high temporal resolution and at several organizational levels from organs to individual plants to field populations of plants. We review the optical sensors and associated data analytics used for measuring morphological, physiological, and biochemical traits of plant leaves on multiple scales. We summarize the characteristics, advantages and limitations of optical sensing and data-processing methods applied in various plant phenotyping scenarios. Finally, we discuss the future prospects of plant leaf phenotyping research. This review aims to help researchers choose appropriate optical sensors and data processing methods to acquire plant leaf phenotypes rapidly,accurately, and cost-effectively.

Optical image encryption algorithm based on a new four-dimensional memristive hyperchaotic system and compressed sensing

Some existing image encryption schemes use simple low-dimensional chaotic systems, which makes the algorithms insecure and vulnerable to brute force attacks and cracking. Some algorithms have issues such as weak correlation with plaintext images, poor image reconstruction quality, and low efficiency in transmission and storage. To solve these issues,this paper proposes an optical image encryption algorithm based on a new four-dimensional memristive hyperchaotic system(4D MHS) and compressed sensing(CS). Firstly, this paper proposes a new 4D MHS, which has larger key space, richer dynamic behavior, and more complex hyperchaotic characteristics. The introduction of CS can reduce the image size and the transmission burden of hardware devices. The introduction of double random phase encoding(DRPE) enables this algorithm has the ability of parallel data processing and multi-dimensional coding space, and the hyperchaotic characteristics of 4D MHS make up for the nonlinear deficiency of DRPE. Secondly, a construction method of the deterministic chaotic measurement matrix(DCMM) is proposed. Using DCMM can not only save a lot of transmission bandwidth and storage space, but also ensure good quality of reconstructed images. Thirdly, the confusion method and diffusion method proposed are related to plaintext images, which require both four hyperchaotic sequences of 4D MHS and row and column keys based on plaintext images. The generation process of hyperchaotic sequences is closely related to the hash value of plaintext images. Therefore, this algorithm has high sensitivity to plaintext images. The experimental testing and comparative analysis results show that proposed algorithm has good security and effectiveness.

Optical anapole modes in hybrid metal–dielectric nanoantenna for near-field enhancement and optical sensing

Metal–dielectric nanostructures in the optical anapole modes are essential for light–matter interactions due to the low material loss and high near-field enhancement. Herein, a hybrid metal–dielectric nanoantenna composed of six wedgeshaped gold(Au) nanoblocks as well as silica(SiO2) and silicon(Si) nanodiscs is designed and analyzed by the finite element method(FEM). The nanoantenna exhibits flexibility in excitation and manipulation of the anapole mode through the strong coupling between the metal and dielectrics, consequently improving the near-field enhancement at the gap. By systematically optimizing the structural parameters, the electric field enhancement factors at wavelengths corresponding to the anapole modes(AM1 and AM2) can be increased to 518 and 1482, respectively. Moreover, the nanoantenna delivers great performance in optical sensing such as a sensitivity of 550 nm/RIU. The results provide guidance and insights into enhancing the coupling between metals and dielectrics for applications such as surface-enhanced Raman scattering and optical sensing.

Optical remote sensing image characteristics of large amplitude convex mode-2 internal solitary waves:an experimental study

A series of experiments are designed to propose a new method to study the characteristics of convex mode-2internal solitary waves(ISWs)in optical remote sensing images using a laboratory-based optical remote sensing simulation platform.The corresponding wave parameters of large-amplitude convex mode-2 ISWs under smooth surfaces are investigated along with the optical remote sensing characteristic parameters.The mode-2 ISWs in the experimentally obtained optical remote sensing image are produced by their overall modulation effect on the water surface,and the extreme points of the gray value of the profile curve of bright-dark stripes appear at the same location as the real optical remote sensing image.The present data extend to a larger range than previous studies,and for the characteristics of large amplitude convex mode-2 ISWs,the experimental results show a second-order dependence of wavelength on amplitude.There is a close relationship between optical remote sensing characteristic parameters and wave parameters of mode-2 ISWs,in which there is a positive linear relationship between the bright-dark spacing and wavelength and a nonlinear relationship with the amplitude,especially when the amplitude is very large,there is a significant increase in bright-dark spacing.

An Intelligent Detection Method for Optical Remote Sensing Images Based on Improved YOLOv7

To address the issue of imbalanced detection performance and detection speed in current mainstream object detection algorithms for optical remote sensing images,this paper proposes a multi-scale object detection model for remote sensing images on complex backgrounds,called DI-YOLO,based on You Only Look Once v7-tiny(YOLOv7-tiny).Firstly,to enhance the model’s ability to capture irregular-shaped objects and deformation features,as well as to extract high-level semantic information,deformable convolutions are used to replace standard convolutions in the original model.Secondly,a Content Coordination Attention Feature Pyramid Network(CCA-FPN)structure is designed to replace the Neck part of the original model,which can further perceive relationships between different pixels,reduce feature loss in remote sensing images,and improve the overall model’s ability to detect multi-scale objects.Thirdly,an Implicitly Efficient Decoupled Head(IEDH)is proposed to increase the model’s flexibility,making it more adaptable to complex detection tasks in various scenarios.Finally,the Smoothed Intersection over Union(SIoU)loss function replaces the Complete Intersection over Union(CIoU)loss function in the original model,resulting in more accurate prediction of bounding boxes and continuous model optimization.Experimental results on the High-Resolution Remote Sensing Detection(HRRSD)dataset demonstrate that the proposed DI-YOLO model outperforms mainstream target detection algorithms in terms of mean Average Precision(mAP)for optical remote sensing image detection.Furthermore,it achieves Frames Per Second(FPS)of 138.9,meeting fast and accurate detection requirements.

Smart Microcavity on the Tip of Multi-Core Optical Fiber for Gas Sensing

We designed and fabricated a smart microcavity sensor with a vertically coupled structure on the end face of a multi-core fiber using two-photon lithography technology. The influence of gap in vertical coupling structure on the resonance characteristics of bonding and anti-bonding modes in the transmission spectrum was studied through simulation and experiments. The results indicate that the bonding and anti-bonding modes generated by the vertical coupling of the two microcavities, as well as the changes in the radius and refractive index of the micro-toroid, and the distance between the microcavities caused by the absorption of vapor during the gas sensing process, exhibit different wavelength shifts for the two resonant modes. Smart microcavity sensors exhibit sensitivity and sensing characteristics. .

Benchmarking dynamic properties of structures using non-contact sensing

Non-contact sensing can be a rapid and convenient alternative for determining structure response compared to conventional instrumentation.Computer vision has been broadly implemented to enable accurate non-contact dynamic response measurements for structures.This study has analyzed the effect of non-contact sensors,including type,frame rate,and data collection platform,on the performance of a novel motion detection technique.Video recordings of a cantilever column were collected using a high-speed camera mounted on a tripod and an unmanned aerial system(UAS)equipped with visual and thermal sensors.The test specimen was subjected to an initial deformation and released.Specimen acceleration data were collected using an accelerometer installed on the cantilever end.The displacement from each non-contact sensor and the acceleration from the contact sensor were analyzed to measure the specimen′s natural frequency and damping ratio.The specimen′s first fundamental frequency and damping ratio results were validated by analyzing acceleration data from the top of the specimen and a finite element model.

Temperature and Salinity Dual-parameter Sensing Based on Forward Brillouin Scattering in 1060-XP SMF

A novel temperature and salinity discriminative sensing method based on forward Brillouin scattering(FBS)in 1060-XP single-mode fiber(SMF)is proposed.The measured frequency shifts corresponding to different radial acoustic modes in 1060-XP SMF show different sensitivities to temperature and salinity.Based on the new phenomenon that different radial acoustic modes have different frequency shift-temperature and frequency shift-salinity coefficients,we propose a novel method for simultaneously measuring temperature and salinity by measuring the frequency shift changes of two FBS scattering peaks.In a proof-of-concept experiment,the temperature and salinity measurement errors are 0.12℃and 0.29%,respectively.The proposed method for simultaneously measuring temperature and salinity has the potential applications such as ocean surveying,food manufacturing and pharmaceutical engineering.

A hardening load transfer function for rock bolts and its calibration using distributed fiber optic sensing

Confinement of rock bolts by the surrounding rock formation has long been recognized as a positive contributor to the pull-out behavior,yet only a few experimental works and analytical models have been reported,most of which are based on the global rock bolt response evaluated in pull-out tests.This paper presents a laboratory experimental setup aiming to capture the rock formation effect,while using distributed fiber optic sensing to quantify the effect of the confinement and the reinforcement pull-out behavior on a more local level.It is shown that the behavior along the sample itself varies,with certain points exhibiting stress drops with crack formation.Some edge effects related to the kinematic freedom of the grout to dilate are also observed.Regardless,it was found that the mid-level response is quite similar to the average response along the sample.The ability to characterize the variation of the response along the sample is one of the many advantages high-resolution fiber optic sensing allows in such investigations.The paper also offers a plasticity-based hardening load transfer function,representing a"slice"of the anchor.The paper describes in detail the development of the model and the calibration/determination of its parameters.The suggested model captures well the coupled behavior in which the pull-out process leads to an increase in the confining stress due to dilative behavior.

Distributed fiber optic sensors for tunnel monitoring:A state-of-the-art review

Distributed fiber optic sensors(DFOSs)possess the capability to measure strain and temperature variations over long distances,demonstrating outstanding potential for monitoring underground infrastructure.This study presents a state-of-the-art review of the DFOS applications for monitoring and assessing the deformation behavior of typical tunnel infrastructure,including bored tunnels,conventional tunnels,as well as immersed and cut-and-cover tunnels.DFOS systems based on Brillouin and Rayleigh scattering principles are both considered.When implementing DFOS monitoring,the fiber optic cable can be primarily installed along transverse and longitudinal directions to(1)measure distributed strains by continuously adhering the fiber to the structure’s surface or embedding it in the lining,or(2)measure point displacements by spot-anchoring it on the lining surface.There are four critical aspects of DFOS monitoring,including proper selection of the sensing fiber,selection of the measuring principle for the specific application,design of an effective sensor layout,and establishment of robust field sensor instrumentation.These four issues are comprehensively discussed,and practical suggestions are provided for the implementation of DFOS in tunnel infrastructure monitoring.

Asymmetrical Fabry-Perot cavity slot micro-ring resonator and its sensing characteristics

To achieve high quality factor and high-sensitivity refractive index sensor,a slot micro-ring resonator(MRR)based on asymmetric Fabry-Perot(FP)cavity was proposed.The structure consisted of a pair of elliptical holes to form an FP cavity and a microring resonator.The two different optical modes generated by the micro-ring resonator were destructively interfered to form a Fano line shape,which improved the system sensitivity while obtaining a higher quality factor and extinction ratio.The transmission principle of the structure was analyzed by the transfer matrix method.The transmission spectrum and mode field distribution of the proposed structure were simulated by the finite difference time domain(FDTD)method,and the key structural parameters affecting the Fano line shape in the device were optimized.The simulation results show that the quality factor of the device reached 22037.1,and the extinction ratio was 23.9 dB.By analyzing the refractive index sensing characteristics,the sensitivity of the structure was 354 nm·RIU−1,and the detection limit of the sensitivity was 2×10−4 RIU.Thus,the proposed compact asymmetric FP cavity slot micro-ring resonator has obvious advantages in sensing applications owing to its excellent performance.

Feasibility study on sinkhole monitoring with fiber optic strain sensing nerves

Anthropogenic activity-induced sinkholes pose a serious threat to building safety and human life nowadays.Real-time detection and early warning of sinkhole formation are a key and urgent problem in urban areas.This paper presents an experimental study to evaluate the feasibility of fiber optic strain sensing nerves in sinkhole monitoring.Combining the artificial neural network(ANN)and particle image velocimetry(PIV)techniques,a series of model tests have been performed to explore the relationship between strain measurements and sinkhole development and to establish a conversion model from strain data to ground settlements.It is demonstrated that the failure mechanism of the soil above the sinkhole developed from a triangle failure plane to a vertical failure plane with increasing collapse volume.Meanwhile,the soil-embedded fiber optic strain sensing nerves allowed deformation monitoring of the ground soil in real time.Furthermore,the characteristics of the measured strain profiles indicate the locations of sinkholes and the associated shear bands.Based on the strain data,the ANN model predicts the ground settlement well.Additionally,micro-anchored fiber optic cables have been proven to increase the soil-to-fiber strain transfer efficiency for large deformation monitoring of ground collapse.

Construction monitoring of lock head base plate using distributed optical fibre sensing technique

The concrete hydration heat release process of the base plate is monitored using Roman optical time domain reflectometry（ROTDR） sensing sensors. The monitoring data shows that the internal maximum temperature of the base plate is about 54 ℃ after the concrete was cured for 120 h. The fiber Bragg grating （FBG） temperature sensors are adopted to measure the surface temperature of the concrete and the temperature results are used to compensate the data measured by the pulse-prepump Brillouin optical time-domain analyzer （PPP-BOTDA） to obtain the real concrete surface strain of the base plate. The monitoring data is analyzed to obtain a clear understanding of the strain state of the base plate under the effect of concrete hydration heat release. The monitoring results demonstrate the potential of distributed optical fibre sensing techniques as a powerful tool in real-time construction monitoring, and also provide an important insight into the design, construction and maintenance of large hydraulic structures.

Mapping the bathymetry of shallow coastal water using singleframe fine-resolution optical remote sensing imagery

This paper presents a bathymetry inversion method using single-frame fine-resolution optical remote sensing imagery based on ocean-wave refraction and shallow-water wave theory. First, the relationship among water depth, wavelength and wave radian frequency in shallow water was deduced based on shallow-water wave theory. Considering the complex wave distribution in the optical remote sensing imagery, Fast Fourier Transform （FFT） and spatial profile measurements were applied for measuring the wavelengths. Then, the wave radian frequency was calculated by analyzing the long-distance fluctuation in the wavelength, which solved a key problem in obtaining the wave radian frequency in a single-frame image. A case study was conducted for Sanya Bay of Hainan Island, China. Single-flame fine-resolution optical remote sensing imagery from QuickBird satellite was used to invert the bathymetry without external input parameters. The result of the digital elevation model （DEM） was evaluated against a sea chart with a scale of 1：25 000. The root-mean-square error of the inverted bathymetry was 1.07 m, and the relative error was 16.2%. Therefore, the proposed method has the advantages including no requirement for true depths and environmental parameters, and is feasible for mapping the bathymetry of shallow coastal water.

A Method for Spaceborne Synthetic Remote Sensing of Atmospheric Aerosol Optical Depth and Vegetation Reflectance

Spaceborne synthetic remote sensing of atmospheric aerosol optical depth and vegetation reflectance is very significant, but it remains to be a question unresolved yet. Based on the property of vegetation reflectance spectra from near ultra violet to near infrared and the sensitivity of outgoing radiance to vegetation reflectance and atmospheric aerosol optical depth, a new method for spaceborne synthetic remote sensing of the reflectance and the depth is proposed, and an iteration correlation inversion algorithm is developed in this paper. According to numerical experiment, effects of radiance error, error in aerosol imaginary index and vegetation medium inhomogeneity on retrieved result are analyzed. Inversion results show that the effect of error in aerosol imaginary index is very important. As the error of aerosol imaginary index is within 0.01, standard errors of aerosol optical depth and vegetation reflectance solutions for 14 spectral channels from 410 nm to 900 nm are respectively less than 0.063 and 0.023. And as the radiance error is within 2%, the standard errors are less than 0.023 and 0.0056.

A two-scale approach for estimating forest aboveground biomass with optical remote sensing images in a subtropical forest of Nepal

Forests account for 80%of the total carbon exchange between the atmosphere and terrestrial ecosystems.Thus,to better manage our responses to global warming,it is important to monitor and assess forest aboveground carbon and forest aboveground biomass(FAGB).Different levels of detail are needed to estimate FAGB at local,regional and national scales.Multi-scale remote sensing analysis from high,medium and coarse spatial resolution data,along with field sampling,is one approach often used.However,the methods developed are still time consuming,expensive,and inconvenient for systematic monitoring,especially for developing countries,as they require vast numbers of field samples for upscaling.Here,we recommend a convenient two-scale approach to estimate FAGB that was tested in our study sites.The study was conducted in the Chitwan district of Nepal using GeoEye-1(0.5 m),Landsat(30 m)and Google Earth very high resolution(GEVHR)Quickbird(0.65 m)images.For the local scale(Kayerkhola watershed),tree crowns of the area were delineated by the object-based image analysis technique on GeoEye images.An overall accuracy of 83%was obtained in the delineation of tree canopy cover(TCC)per plot.A TCC vs.FAGB model was developed based on the TCC estimations from GeoEye and FAGB measurements from field sample plots.A coefficient of determination(R2)of 0.76 was obtained in the modelling,and a value of 0.83 was obtained in the validation of the model.To upscale FAGB to the entire district,open source GEVHR images were used as virtual field plots.We delineated their TCC values and then calculated FAGB based on a TCC versus FAGB model.Using the multivariate adaptive regression splines machine learning algorithm,we developed a model from the relationship between the FAGB of GEVHR virtual plots with predictor parameters from Landsat 8 bands and vegetation indices.The model was then used to extrapolate FAGB to the entire district.This approach considerably reduced the need for field data and commercial very high resolution imagery while achieving two-scale forest information and FAGB estimates at high resolution(30 m)and accuracy(R2=0.76 and 0.7)with minimal error(RMSE=64 and 38 tons ha-1)at local and regional scales.This methodology is a promising technique for cost-effective FAGB and carbon estimations and can be replicated with limited resources and time.The method is especially applicable for developing countries that have low budgets for carbon estimations,and it is also applicable to the Reducing Emissions from Deforestation and Forest Degradation(REDD?)monitoring reporting and verification processes.

Experimental investigation on the optical remote sensing images of internal solitary waves with a smooth surface

The parameter inversion of internal solitary waves (ISWs) based on optical remote sensing images is a key work. A new approach is proposed and demonstrated for simulating the optical remote sensing images of ISWs with a smooth surface in the laboratory. An optical remote sensing simulation system used to detect ISWs is constructed by a two-dimensional ISW flume, a LED (light emitting diode) light source and two CCD (charge coupled device) cameras. The optical remote sensing images of the horizontal surface and ISWs propagation images of a vertical side are detected simultaneously, which aims to explore the response of optical remote sensing corresponding to ISWs with the smooth surface. The results show that during the propagation of ISWs, dark pattern images are obtained by CCD 1 camera. The characteristics of the dark patterns vary along with the incident angle of the light source. The characteristic parameters of the optical remote sensing images correspond to the wave factors of vertical profiles. The experiment also shows a positive correlation between the dark pattern width and the half wave width under different amplitudes of ISWs. The system has the advantages of clear phenomenon and high repeatability, which provides the scientific basis for quantitative investigation on imaging mechanism of ISW by optical remote sensing.

Structural Deformation Monitoring of Flight Vehicles Based on Optical Fiber Sensing Technology:A Review and Future Perspectives

Structural deformation monitoring of flight vehicles based on optical fiber sensing(OFS)technology has been a focus of research in the field of aerospace.After nearly 30 years of research and development,Chinese and international researchers have made significant advances in the areas of theory and methods,technology and systems,and ground experiments and flight tests.These advances have led to the development of OFS technology from the laboratory research stage to the engineering application stage.However,a few problems encountered in practical applications limit the wider application and further development of this technology,and thus urgently require solutions.This paper reviews the history of research on the deformation monitoring of flight vehicles.It examines various aspects of OFS-based deformation monitoring including the main varieties of OFS technology,technical advantages and disadvantages,suitability in aerospace applications,deformation reconstruction algorithms,and typical applications.This paper points out the key unresolved problems and the main evolution paradigms of engineering applications.It further discusses future development directions from the perspectives of an evolution paradigm,standardization,new materials,intelligentization,and collaboration.

Two-dimensional distributed strain sensing with an Archimedean spiral arrangement in optical frequency domain reflectometry

We demonstrate a distributed two-dimensional(2D)strain-sensing system in optical frequency domain reflectometry(OFDR)with an Archimedean spiral arrangement of the sensing fiber.The Archimedean spiral describes a simple relationship between the radial radius and polar angle,such that each circle(the polar angle from0 to 2π)can sense the 2D strain in all directions.The strain between two adjacent circles can also be easily obtained because an Archimedean spiral facilitates sensing of every angle covering the full 2D range.Based on the mathematical relation of Archimedean spirals,we deduce the relationship between the one-dimensional position of the sensing fiber and 2D distribution in polar coordinates.The results of the experiment show that an Archimedean spiral arrangement system can achieve 2D strain sensing with different strain load angles.