Optical Patch Research Articles

Cracks monitoring in reinforced concrete structure with optical fibers patch

In the context of the necessity to monitor civil engineering structures in order to prevent incidents, this paper presents the performance of an innovative optical fibers patch sensor. The patch comprises a tissue containing an optical fiber network. The sensor is attached to the concrete surface and capable of detecting opening cracks in the order of 0.01 mm with good precision, thus satisfying the regulatory limits. The sensor is capable of precisely following the evolution of the crack, including cyclic changes in crack width. The procedure presented in this paper includes a preliminary investigation into the influence of the fibers’ diameter and the density of fibers in the patch, as well as the method of attachment to the concrete’s surface with the objective of identifying an optimal sensor configuration. Subsequently, a series of laboratory tests were conducted on reinforced concrete samples of varying dimensions. Comparisons and verifications of measurements of optical fiber patch sensors are conducted via Digital Image Correlation process. The conclusions of this paper permit the consideration of the use of this novel sensor on and within real reinforced concrete structures.

Antenna-enhanced high-resistance photovoltaic infrared detectors based on quantum ratchet architecture

We demonstrate a quantum ratchet detector, which is a high-resistance photovoltaic mid-infrared detector based on an engineered spatial arrangement of subbands. In photovoltaic quantum-well photodetectors, in which unidirectional photocurrent is generated by asymmetric quantum-well structures, maximization of device resistance by suppressing undesired electron transports is crucial for minimizing noise. A semi-quantitative guideline suggests the significance of spatial separation between wavefunctions for reducing the conductance from the ground state. Here, we employ a step quantum well made of a shallow floor and a deep well. Photoexcited electrons are quickly transferred to a separated location from the ground state through fast resonant tunneling and phonon scattering, and then they are allowed to flow in only one direction. This architecture is made possible by the use of a GaAs/AlGaAs material system, and it achieves a resistance as high as 6.0 × 104 Ωcm2 with a single-period structure. Combined with optical patch antennas for responsivity enhancement, we demonstrate a maximum background-limited specific detectivity of 6.8 × 1010 cmHz1/2/W at 6.4 μm, 77 K for normal incidence, and a background-limited-infrared-photodetector temperature of 98 K.

Passively mode-locked fiber laser based on ethylene glycol

A passively mode-locked Er-doped fiber laser employing ethylene glycol as a saturable absorber (SA) has been proposed and experimentally validated. The ethylene glycol-SA is fabricated by filling the space between the end facets of two optical patch cords inserted into a cannula with ethylene glycol. Utilizing such an SA with a modulation depth of 9.6 %, stable mode-locking operation are manifested in the laser cavity, including the production of bright pulse and bright-dark pulse pair. The generated bright pulse has a center wavelength at 1562.28 nm, a 3 dB bandwidth of 0.21 nm, a repetition frequency of 20.24 MHz, and a signal-to-noise ratio (SNR) of 57 dB. Through precisely manipulating the polarization state and the pump power in the laser cavity, the bright-dark pulse pairs can be successfully attained at the same repetition frequency. This is the first demonstration of a passively mode-locked fiber laser utilizing ethylene glycol as the saturable absorber. Due to their superior optical absorption characteristics, low cost, simplicity in fabrication and non-volatile nature, ethylene glycol-SAs have enormous potential in the field of nonlinear optics and ultrafast photonics.

Surface-Enhanced Raman Scattering in Silver-Coated Suspended-Core Fiber.

In this paper, the silver-coated large-core suspended-core fiber (LSCF) probe was fabricated by the dynamic chemical liquid phase deposition method for surface-enhanced Raman scattering (SERS) sensing. The 4-mercaptophenylboronic acid (4-MPBA) monolayer was assembled in the LSCF as the recognition monolayer. Taking advantage of the appropriate core size of the LSCF, a custom-made Y-type optical fiber patch cable was utilized to connect the semiconductor laser, Raman spectrometer, and the proposed fiber SERS probe. The SERS signal is propagated in the silver-coated air channels, which can effectively reduce the Raman and fluorescence background of the silica core. Experiments were performed to measure the Raman scattering spectra of the 4-MPBA in the silver-coated LSCF in a non-enhanced and enhanced case. The experiment results showed that the Raman signal strength was enhanced more than 6 times by the surface plasmon resonance compared with the non-enhanced case. The proposed LSCF for SERS sensing technology provides huge research value for the fiber SERS probes in biomedicine and environmental science. The combination of SERS and microstructured optical fibers offers a potential approach for SERS detection.

Optical nano patch antenna for terahertz applications with graphene

Nano optical crescent patch antenna for Terahertz applications using Graphene is designed in this paper. The antenna is designed at 7.28 THZ with several substrates material as PTFE (²r = 2.1), polymide (²r = 3.5), RO3003 (²r = 3), RO4003 (²r = 3.4) and Arlon AD (²r = 2.5). Graphene is the material patch used with different properties such as chemical potential µc = 0.2 eV, relaxation time τ = 1 ps and thickness of 60 nm to achieve a high gain and bandwidth. We obtained a very good performance of crescent antenna at 7.28 THZ with −37.962 dB, 7.124 dBi, 1.767 THZ of return loss, gain and bandwidth respectively which is very satisfactory for terahertz transmission between [0.1-10] THZ.

SAR-optical feature matching: A large-scale patch dataset and a deep local descriptor

Image matching is a key prerequisite for image fusion. Currently, deep learning methods had shown great potential in matching. However, these methods mainly focus on optical images or area-based matching and their performance highly relies on the diversity of the training sets. This paper makes two major contributions to the synthetic aperture radar (SAR)-optical image feature matching task. First, we create a high-quality open SAR and optical patch dataset called SOPatch, which contains more than 650000 matching patch pairs. SOPatch is generated from a variety of satellite images (Sentinel-1, Sentinel-2, Gaofen-3, etc.) and contains a rich set of features (mountains, lakes, buildings, farmland, bare land, etc.), which can provide robustness to sensor and localization changes and train a model with good generalization ability. Similar to the HPatches (Balntas et al., 2017), we also add position and rotation noise to make the dataset more realistic and robust. Second, a local descriptor for SAR-optical matching called SODescNet is proposed. We first weaken the speckle noise of SAR by multiscale dilated convolution and channel attention mechanisms and then use DenseNet-CSP (cross stage partial) as the backbone for feature description. To reduce the difficulty of network training, we first use a Siamese network for pre-training and then fine-tune it via a pseudo-Siamese network. Extensive experiments show that our SODescNet is comparable or superior to state-of-the-arts.

Postures anomaly tracking and prediction learning model over crowd data analytics.

Innovative technology and improvements in intelligent machinery, transportation facilities, emergency systems, and educational services define the modern era. It is difficult to comprehend the scenario, do crowd analysis, and observe persons. For e-learning-based multiobject tracking and predication framework for crowd data via multilayer perceptron, this article recommends an organized method that takes e-learning crowd-based type data as input, based on usual and abnormal actions and activities. After that, super pixel and fuzzy c mean, for features extraction, we used fused dense optical flow and gradient patches, and for multiobject tracking, we applied a compressive tracking algorithm and Taylor series predictive tracking approach. The next step is to find the mean, variance, speed, and frame occupancy utilized for trajectory extraction. To reduce data complexity and optimization, we applied T-distributed stochastic neighbor embedding (t-SNE). For predicting normal and abnormal action in e-learning-based crowd data, we used multilayer perceptron (MLP) to classify numerous classes. We used the three-crowd activity University of California San Diego, Department of Pediatrics (USCD-Ped), Shanghai tech, and Indian Institute of Technology Bombay (IITB) corridor datasets for experimental estimation based on human and nonhuman-based videos. We achieve a mean accuracy of 87.00%, USCD-Ped, Shanghai tech for 85.75%, and IITB corridor of 88.00% datasets.

GAN-based SAR and optical image translation for wildfire impact assessment using multi-source remote sensing data

Despite the popularity and success in burned area detection and assessment, multispectral satellite images are often affected by poor sunlight-illumination conditions, particularly at high latitudes. Given that Synthetic Aperture Radar (SAR) can effectively penetrate clouds and collect images in all-weather conditions during day and night, the complementary use of optical and SAR data can be helpful for remote-sensing measurements and assessments of burned sites. Nevertheless, the widely used burn-sensitive spectral indices hardly help analyze SAR data due to the inherent difference between optical and SAR sensors in physical imaging mechanisms. In this study, we aim to leverage multi-source data for burned area mapping and burn severity assessment by translating SAR images into optical images using ResNet-based Pix2Pix model. Experiments were performed on 8669 pairs of bitemporal Sentinel-1 SAR and Sentinel-2 optical patches over 304 large wildfire events in Canada with a wide range of land covers. The translated optical images from SAR data occupied similar spectral properties to real optical observations and the corresponding generated spectral indices (i.e., delta Normalized Burn Ratio (dNBR), relative dNBR, and Relativized Burn Ratio) also showed high agreement with real optical indices. In terms of burned area detection using the generated indices, their medium values of the area under the receiver operating characteristics curve (AUC) were over 85%, achieving promising performance that outperformed the SAR-based index. Burn severity maps derived from multi-source data achieved a relatively high Kappa coefficient of 0.77. Results showed the feasibility and effectiveness of GAN-based SAR-to-optical translation for wildfire impact assessment, paving the way for the combined use of optical and SAR data.

Learning to Find the Optimal Correspondence Between SAR and Optical Image Patches

Learning to Find the Optimal Correspondence Between SAR and Optical Image Patches

Calibration of the ICARUS cryogenic photo-detection system at FNAL

The calibration of the ICARUS photo-detection system is based on a low power laser diode at 405 nm. Laser pulses arrive to one optical switch and then are sent to 36 UHV flanges, by 20 meters long optical patches. Light is then delivered to the ten PMTs connected to a single flange, by 7 m long injection optical patches. Extensive tests of the used components and care in the design of the optical system have guaranteed to each PMT a sizeable calibration signal with minimal distortion, with respect to the original one. Gain equalization of PMTs has reached a 1% resolution. In this procedure data from background photons were also used. The distribution of the PMTs’ signal arrival time has a distribution with a resolution less than 1 ns, thus allowing a good determination of the absolute event timing. The status of the laser calibration system with its possible upgrades will be reported.

PM-Net: A Multi-Level Keypoints Detector and Patch Feature Learning Network for Optical and SAR Image Matching

Due to the differences in radiation and geometric characteristics of optical and synthetic aperture radar (SAR) images, there is still a huge challenge for accurate matching. In this paper, we propose a patch-matching network (PM-Net) to improve the matching performance of optical and SAR images. First, a multi-level keypoints detector (MKD) with fused high-level and low-level features is presented to extract more robust keypoints from optical and SAR images. Second, we use a two-channel network structure to improve the image patch matching performance. Benefiting from this design, the proposed method can directly learn the similarity between optical and SAR image patches without manually designing features and descriptors. Finally, the MKD and two-channel net-work are trained separately on GL3D and QXS-SAROPT data sets, and the PM-Net is tested on multiple pairs of optical and SAR images. The experimental results demonstrate that the proposed method outperforms four advanced image matching networks on qualitative and quantitative assessments. The quantitative experiment results show that using our method correct matching points numbers are increased by more than 1.15 times, the value of F1-measure is raised by an average of 7.4% and the root mean squared error (RMSE) is reduced by more than 15.3%. The advantages of MKD and the two-channel network are also verified through ablation experiments.

Combined application of an optical sensor and a common non-destructive instrument for corrosion monitoring in reinforced concrete members

The reliable monitoring of the progression or extent of pitting-type corrosion in reinforced concrete members requires great effort due to non-uniformity. This paper presents a new approach for measuring the progression and extent of pitting-type (non-uniform) corrosion in reinforced concrete members by combining both non-destructive testing with optical fibre sensing results. Accelerated corrosion on the reinforcement (rebar) of the specimen was produced artificially using an alternate wetting and drying process in sodium chloride solution with 7.5% and 10% concentrations. The multimode optical fibre patch cords were attached to the reinforcement in three different arrangements in a single specimen (normal optical patch cord and loose and firm microbend patch cords). Intensity loss in a propagating light travelling inside an optical fibre takes place due to the development of pressure from surrounding concrete on the fibre from rust formation (corrosion). The study also describes the effect of corrosion in half-cell potentiometer, chloride ion resistivity and indirect pulse velocity readings obtained from the specimen. A later study found that the use of an optical microbend sensor is an effective alternate method for determining the starting time of initial depassivation in the reinforcement and validates it using half-cell potentiometer values. The study also developed models to predict overall mass loss in the rebar due to corrosion using combined non-destructive testing methods.

Dual-band optical imaging system-integrated patch antenna based on anisotropic fractal for earth-observation CubeSats

This paper presents a dual-band optical imaging system-integrated patch antenna based on an anisotropic fractal for Earth-observation CubeSats. The antenna operates at S-band frequencies. The size of the antenna is chosen to match the end face of the CubeSats. The antenna's main characteristics are studied using simulation in Computer Simulation Technology (CST) software; prototypes are developed to verify these characteristics. Double-sided Glass Epoxy Sheet FR-4 is used to develop the antenna prototype. By studying the antenna prototype, realized gain up to 4 dBi and 2.15 dBi at two resonant frequencies of the antennas with different substrate heights are obtained. Small size, low cost, dual-band, and integration with the imaging system allow achieving the entire CubeSat system's efficiency.

Self-Distillation Feature Learning Network for Optical and SAR Image Registration

Optical and SAR image registration is important for multi-modal remote sensing image information fusion. Recently, deep matching networks have shown better performances than traditional methods on image matching. However, due to significant differences between optical and SAR images, the performances of existing deep learning methods still need to be further improved. This paper proposes a self-distillation feature learning network (SDNet) for optical and SAR image registration, improving performance from network structure and network optimization. Firstly, we explore the impact of different weight-sharing strategies on optical and SAR image matching. Then, we design a partially unshared feature learning network for multi-modal image feature learning. It has fewer parameters than the fully unshared network and has more flexibility than the fully shared network. Additionally, the limited binary supervised information (matching or non-matching) is insufficient to train the deep matching networks for optical-SAR image registration. Thus, we propose a self-distillation feature learning method to exploit more similarity information for deep network optimization enhancing, such as the similarity ordering between a series of non-matching patch-pairs. The exploited rich similarity information will significantly enhance network training and improve matching accuracy. Finally, considering that existing deep learning methods brute-force constrain the features of the matching optical and SAR image patches are similar, which will be lost many discriminative information, degenerating matching performances. Thus, we build an auxiliary task reconstruction learning to optimize the feature learning network to keep more discriminative information. Extensive experiments demonstrate the effectiveness of our proposed method on multi-modal image registration.

Detecting subsurface drainage pipes using a fully convolutional network with optical images

More than half of croplands in the Midwestern United States are equipped with subsurface drainage pipes to reduce excess water in productive but wet areas. The use of drainage systems not only reduces subsurface water table to prevent waterlogging and flooding but also increases nutrient losses by developing artificial preferential flow paths. The exact locations of subsurface drainage pipes are thus imperative to manage and monitor water quality and nonpoint source pollution. However, such data are not widely available due to private ownership. Previous studies used conventional image filtering methods, thermal images, and ground penetration radar to detect subsurface drainage pipes. Due to surface features, such as furrow and depressions, and their limited data availability, these experiments did not provide a robust approach to identify subsurface drainage pipes over a large area. To overcome these limitations, in this study, we propose a subsurface drainage pipe detection approach based on deep learning with optical images. Our deep learning approach uses a fully convolution network (FCN) architecture that takes an optical image patch as an input and gives an output of pixel-wise drainage pipe detection map. The FCN was trained and validated using optical image datasets obtained from a freeware Google Earth that provides temporally and spatially abundant data. The trained FCN was then applied to large-scale drainage pipe detection tasks to evaluate its performance. The performance comparison between the proposed deep learning approach and conventional image processing techniques (Sobel and Canny edge detection methods) was also carried out. The results demonstrate that the proposed deep learning approach shows accurate and robust drain line detection performance with an average Dice coefficient of 0.58 for validation sets, providing superior performance over the conventional image processing techniques.

A Spectroscopic Diffuse Optical Tomography System for the Continuous 3-D Functional Imaging of Tissue—A Phantom Study

This article demonstrates a spectroscopic diffuse optical tomography (DOT) system for the noninvasive continuous imaging of tissue. The system was built on an embedded system architecture along with an algorithm that performs entire system control, data acquisition, noise-free lock-in detection, and wireless data transmission to a host computer, equipped with a graphics processing unit (GPU). The GPU system executes a fast 3-D DOT spectroscopic image reconstruction algorithm. The algorithm computes Jacobians and solves four different depth-sensitive inverse problems of image reconstruction. The host computer sends commands to the DOT server system and collects data, and simultaneously performs high-speed 3-D spectroscopic image reconstruction to display images in real-time. We designed a configurable optical fiber patch to collect depth-resolved diffused light exiting the imaging media by placing the patch on the surface. The simulation and experimental results from two setups showed the spectroscopic imaging capability of the system to map the concentration of oxyhemoglobin, deoxyhemoglobin, and water, and to characterize the tumor located deep inside the tissue. The proposed system uses low-cost LEDs, photodiode detectors, embedded system, and algorithms to perform high-speed 3-D functional imaging and can be further miniaturized. Our promising results pave the way for the development of a low-cost hand-held spectroscopic DOT system for the real-time onsite functional imaging of biological tissue.

Optical Convolutional Neural Network With WDM-Based Optical Patching and Microring Weighting Banks

We propose an optical convolutional neural network (OCNN) architecture for high-speed and energy-efficient deep learning accelerators. The WDM-based optical patching scheme (WDM-OPS) is adopted as the data-feeding structure for its superior energy efficiency and the microring banks are used for the large-scale weighting and summing (the computing core). We thoroughly investigate the performance (including prediction accuracy, speed, and energy efficiency) of this architecture in different system defects. The results indicate that, the prediction accuracy of OCNN can reach 97% in the MNIST dataset with a computing speed of over 100 TMAC/s on condition of achievable low insertion loss. It is also observed that the WDM-OPS notably reduces the energy consumption of the electro-optic modulation and thus the OCNN becomes an exceptionally energy-efficient architecture among several well-known optical architectures. In the evaluations, instead of merely considering the computing core, we take the holistic optical system including lasers, electro-optic modulators, data preprocessing, photodetection and transimpedance amplification into consideration. Therefore, this work provides a potential guide for the systematic implementation of the OCNN architecture.

TRPV4 blockade suppresses atrial fibrillation in sterile pericarditis rats.

Atrial fibrillation (AF) commonly occurs after surgery and is associated with atrial remodeling. TRPV4 is functionally expressed in the heart, and its activation affects cardiac structure and functions. We hypothesized that TRPV4 blockade alleviates atrial remodeling and reduces AF induction in sterile pericarditis (SP) rats. TRPV4 antagonist GSK2193874 or vehicle was orally administered 1 day before pericardiotomy. AF susceptibility and atrial function were assessed using in vivo electrophysiology, ex vivo optical mapping, patch clamp, and molecular biology on day 3 after surgery. TRPV4 expression increased in the atria of SP rats and patients with AF. GSK2193874 significantly reduced AF vulnerability in vivo and the frequency of atrial ectopy and AF with a reentrant pattern ex vivo. Mechanistically, GSK2193874 reversed the abnormal action potential duration (APD) prolongation in atrial myocytes through the regulation of voltage-gated K+ currents (IK); reduced the activation of atrial fibroblasts by inhibiting P38, AKT, and STAT3 pathways; and alleviated the infiltration of immune cells. Our results reveal that TRPV4 blockade prevented abnormal changes in atrial myocyte electrophysiology and ameliorated atrial fibrosis and inflammation in SP rats; therefore, it might be a promising strategy to treat AF, particularly postoperative AF.

BUILDING DETECTION FROM SAR IMAGES USING UNET DEEP LEARNING METHOD

Abstract. SAR images are different from the optical images in terms of image properties with the values of scattering instead of reflectance. This makes SAR images difficult to apply the traditional object detection methodologies. In recent years, deep learning models are frequently used in segmentation and object detection purposes. In this study, we have investigated the potential of U-Net models for building detection from SAR and optical image fusion. The datasets used are Sentinel 1 SAR and Sentinel-2 multispectral images, provided from ‘SpaceNet 6 Multi Sensor All-Weather Mapping’ challenge. These images cover an area of 120 km2 in Rotterdam, the Netherlands. As training datasets 20 pieces of 900 by 900 pixel sized HV polarized and optical image patches have been used together. The calculated loss value is 0.4 and the accuracy is 81%.

On the local behavior of spaces of range image patches

We focus on the quantitative and local topological properties of range images. We consider the spaces Mm of m × m high-contrast patches of range images for m= 3, 5, 7, 9, 11. Using computational topological tools to analyze range image patches, we detect that M3 (M9, M11) has core subsets with the topology of a circle, M3, M5, M7, M9 and that M11 have some subspaces with the topology of a Klein bottle. We also discover that the largest subspace with the Klein bottle’s topology decreases as the measurements of patches increase, which generalizes the results in the paper of H. Adams and G. Carlsson, and demonstrates properties among optical images and range image patches, which are more similar than those established by Lee et al.