Search

This work presents a wide-area highway monitoring system based on distributed fiber-optic sensing (DFOS) as a cost-effective way of gathering traffic information at numerous sensing points along a fiber cable. The primary advantage of our proposed DFOS system is that it utilizes an existing fiber cable buried beneath the highway to detect and localize the vibrations of passing vehicles. Each section along the fiber cable acts as a sensing point and registers the vibration of nearby vehicles. The amplitude and location of vibrations as measured by DFOS can supplement the information obtained from existing point sensor systems (traffic camera, inductive loop detector) which are typically installed hundreds of meters apart. We trained a neural network for speed estimation ( SpeedNet) and also proposed novel solutions to some of the challenges posed when using DFOS to monitor traffic. To demonstrate the potential of DFOS, we conducted a field test for two days on a 45-km section of the Tomei expressway in Japan. Our proposed SpeedNet model estimated average traffic speeds every minute for an overall accuracy of over 90% as compared with existing loop detector-based sensors. As cameras suffer from weather and intensity changes, and loop detectors can be difficult to install at multiple locations, monitoring traffic using DFOS over an existing fiber-optic cable shows tremendous potential.

We review sensing fusion results of integrating fiber sensing with video for machine-learning-based localization and classification of impulsive acoustic event detection. Classification accuracy >97% was achieved on aerial coils, and >99% using fiber-based signal enhancers.

We report the first distributed acoustic sensing (DAS) results over >1,000 km on a field-lab hybrid link using chirped-pulses with correlation detection and 20× frequency-diversity, achieving a sensitivity of 100 p ∈ / Hz at 20-meters spatial resolution.

We present a manhole localization method based on distributed fiber optic sensing and weakly supervised machine learning techniques. For the first time to our knowledge, ambient environment data is used for underground cable mapping with the promise of enhancing operational efficiency and reducing field work. To effectively accommodate the weak informativeness of ambient data, a selective data sampling scheme and an attention-based deep multiple instance classification model are adopted, which only requires weakly annotated data. The proposed approach is validated on field data collected by a fiber sensing system over multiple existing fiber networks.

To the best of our knowledge, we present the first field trial of distributed fiber optical sensing (DFOS) and high-speed communication, comprising a coexisting system, over an operation telecom network. Using probabilistic-shaped (PS) DP-144QAM, a 36.8 Tb/s with an 8.28-b/s/Hz spectral efficiency (SE) (48-Gbaud channels, 50-GHz channel spacing) was achieved. Employing DFOS technology, road traffic, i.e., vehicle speed and vehicle density, were sensed with 98.5% and 94.5% accuracies, respectively, as compared to video analytics. Additionally, road conditions, i.e., roughness level was sensed with >85% accuracy via a machine learning based classifier.

We demonstrate a new application of fiber-optic-sensing and machine learning techniques for vehicle run-off-road events detection to enhance roadway safety and efficiency. The proposed approach achieves high accuracy in a testbed under various experimental conditions.

We report the field trial results of monitoring abnormal activities near deployed cable with fiber-optic-sensing technology for cable protection. Detection and position determination of abnormal events and evaluating the threat to the cable is realized.

We report the distributed-fiber-sensing field trial results over a 5G-transport-network. A standard communication fiber is used with real-time AI processing for cable self-protection, cable- cut threat assessment and road traffic monitoring in a long-term continuous test.

We demonstrate vibration detection and localization based on extracting optical phase from the DSP elements of a coherent receiver in bidirectional WDM transmission of 200-Gb/s DP-16QAM over 380 km of installed field fiber.

We demonstrate fiber optic sensing systems in a distributed fiber sensor network built on existing telecom infrastructure to detect temperature, acoustic effects, vehicle traffic, etc. Measurements are also demonstrated with different network topologies and simultaneously sensing four fiber routes with one system.

To the best of our knowledge, we present the first underground fiber cable position detection methods using distributed fiber optic sensing (DFOS) technology. Meter level localization accuracy is achieved in the results.

We demonstrated for the first time that geographic locations on deployed fiber cables can be determined accurately by using OTDR distances. The method involves vibration stimulation near deployed cables and distributed fiber optical sensing technology.

We proposed and demonstrated a new machine learning algorithm for wavelength modulation spectroscopy to enhance the accuracy of fire detection. The result shows more than 8% of accuracy improvement by analyzing CO/CO 2 2f signals.

We propose a novel multi-parameter sensing technique based on a Brillouin optical time domain reflectometry in the elliptical-core few-mode fiber, using higher-order optical and acoustic modes. Multiple Brillouin peaks are observed for the back scattering of both the LP01 mode and LP11 mode. We characterize the temperature and strain coefficients for various optical–acoustic mode pairs. By selecting the proper combination of modes pairs, the performance of multi-parameter sensing can be optimized. Distributed sensing of temperature and strain is demonstrated over a 0.5-km elliptical-core few-mode fiber, with the discriminative uncertainty of 0.28°C and 5.81 με for temperature and strain, respectively Refereed/Peer-reviewed

Angiopoietin-like protein 2 (Angptl2) is regarded as a proinflammatory factor in the pathogenesis of atherosclerosis and is expressed at high levels in patients with coronary artery disease. However, direct evidence of Angptl2 in acute coronary syndrome (ACS) is lacking. Our study was designed to investigate a possible relationship between serum Angptl2 and ACS.We evaluated 251 consecutive patients undergoing coronary angiography, consisting of 132 patients with ACS (unstable angina pectoris n = 60, acute myocardial infarction n = 72), 50 patients with stable angina pectoris, and 69 control patients. Serum Angptl2 concentration was measured in peripheral venous blood by an enzyme-linked immunosorbent assay.Serum Angptl2 levels were significant higher in patients with ACS than in those with stable angina (p0.05) or controls (p0.001). The difference between angplt2 levels in unstable angina and acute myocardial infarction subgroups was statistically insignificant (p = 0.831). In multivariable logistic regression models, using quartiles of Angptl2, Angptl2 was closely associated with ACS following adjustment of age, gender, established risk factors and high sensitivity C-reactive protein levels (odds ratio for quartile 4 vs. quartile 1: 10.182, 95% confidence interval 2.440-42.485, p = 0.001).Serum Angptl2 is a new candidate biomarker for risk stratification of ACS.

We propose a single-ended Brillouin-based sensor in elliptical-core few-mode optical fiber for multi-parameter measurement using spontaneousBrillouin scattering. Distributed sensing of temperature and strain is demonstrated over 0.5 km elliptical-core few-mode fiber. Refereed/Peer-reviewed