Read our Optical Networking and Sensing publications from our team of researchers. We are leading world-class research into the next generation of optical networks and sensing systems that will power ICT-based social solutions for years. We advance globally acknowledged innovation by engaging in visionary theoretical research, pioneering experiments, and leading technology field trials. Our work not only foresees the future but also transforms it into today’s reality.
For the first time, we demonstrate detection and classification of rain intensity using Distributed Acoustic Sensing (DAS). An artificial neural network was applied for rain intensity classification and high precision of over 96% was achieved.
We review multiple use cases over deployed networks including co-existing sensing/data transmission, cable cut prevention and perimeter intrusion detection to realize telecom infrastructure can be sensing backbones instead of the sole function of data transmission.
We report distributed-fiber-optic-sensing results on impulsive acoustic events localization/classification over telecom networks. A deep-learning-based model was trained to classify starter-gun and fireworks signatures with high accuracy of > 99% using fiber-based-signal-enhancer and >97% using aerial coils.
We review recent advances aimed at increasing the reach of distributed fiber optic sensing with simultaneous data transmission. We review two methods based on measurement of accumulated phase on telecom signals, and chirp-pulsed DAS with inline amplification and frequency diversity.
We propose a new method to detect acoustic signals by matching distributed acoustic sensing data with simulation. In the simulation of the dynamic strain on an optical fiber, the optical fiber layouts and the gauge length are properly incorporated. We apply the proposed method to the acoustic-source localization and demonstrate the method localizes the source accurately even under the layouts which include the straight optical fiber for the sensing points with the large gauge-length settings.
Recently, the distributed fiber optic sensing (DFOS) techniques have advanced rapidly. There emerges various types of DFOS sensors that can monitor physical parameters such as temperature, strain, and vibration. With these DFOS sensors deployed, the telecom networks are capable of offering additional services beyond communications, such as monitoring road traffic condition, monitoring utility pole health, monitoring city noise and accident, thus evolving to a new paradigm of Infrastructure-as-a-Sensor (IaaSr) or Network-as-a-Sensor (NaaSr). When telecom network carriers upgrade their infrastructures with DFOS sensors to provide such IaaSr/NaaSr services, there will arise a series of critical challenges: (1) where to place the DFOS sensors, and (2) how to provision the DFOS sensing fiber routes to cover the whole network infrastructures with the minimum number of DFOS sensors? We name this as the DFOS placement problem. In this paper, we prove that the DFOS placement problem is an NP-hard problem, and we analyze the upper bound of the number of DFOS sensors used. To facilitate the optimal solution, we formulate the DFOS placement problem with an Integer Linear Programming model that aims at minimizing the number of DFOS sensors used. Furthermore, we propose a cost-efficient heuristic solution, called Explore-and-Pick (EnP), which can achieve a close-to-optimal performance in a fast manner. We analyze the approximation ratio and the computational complexity of the proposed EnP algorithm. In addition, we conduct comprehensive simulations to evaluate the performance of the proposed solutions. Simulation results show that the EnP algorithm can outperform the baseline algorithm by 16% in average and 26% at best, and it achieves a performance that is close to the optimal result obtained by ILP.
We experimentally demonstrated the real-time operation of a photonic neuron with a self-connection, a prerequisite for integrated recurrent neural networks (RNNs). After studying two applications, we propose a photonics-assisted platform for time series prediction and classification.
We propose a reinforcement learning-based approach to query object localization, for which an agent is trained to localize objects of interest specified by a small exemplary set. We learn a transferable reward signal formulated using the exemplary set by ordinal metric learning. Our proposed method enables test-time policy adaptation to new environments where the reward signals are not readily available and outperforms fine-tuning approaches that are limited to annotated images. In addition, the transferable reward allows repurposing the trained agent from one specific class to another class. Experiments on corrupted MNIST, CU-Birds, and COCO datasets demonstrate the effectiveness of our approach.
This paper studies zero-shot domain adaptation where each domain is indexed on a multi-dimensional array, and we only have data from a small subset of domains. Our goal is to produce predictors that perform well on unseen domains. We propose a model which consists of a domain-invariant latent representation layer and a domain-specific linear prediction layer with a low-rank tensor structure. Theoretically, we present explicit sample complexity bounds to characterize the prediction error on unseen domains in terms of the number of domains with training data and the number of data per domain. To our knowledge, this is the first finite-sample guarantee for zero-shot domain adaptation. In addition, we provide experiments on two-way MNIST and four-way fiber sensing datasets to demonstrate the effectiveness of our proposed model.
In pursuance of the unused spectrum in higher frequencies, millimeter wave (mmWave) bands have a pivotal role. However, the high path-loss and poor scattering associated with mmWave communications highlight the necessity of employing effective beamforming techniques. In order to efficiently search for the beam to serve a user and to jointly serve multiple users it is often required to use a composite beam which consists of multiple disjoint lobes. A composite beam covers multiple desired angular coverage intervals (ACIs) and ideally has maximum and uniform gain (smoothness) within each desired ACI, negligible gain (leakage) outside the desired ACIs, and sharp edges. We propose an algorithm for designing such ideal composite codebook by providing an analytical closed-form solution with low computational complexity. There is a fundamental trade-off between the gain, leakage and smoothness of the beams. Our design allows to achieve different values in such trade-off based on changing the design parameters. We highlight the shortcomings of the uniform linear arrays (ULAs) in building arbitrary composite beams. Consequently, we use a recently introduced twin-ULA (TULA) antenna structure to effectively resolve these inefficiencies. Numerical results are used to validate the theoretical findings.
