Optical Networking and SensingRead 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.

Posts

Detection of Waves and Sea-Surface Vessels via Time Domain Only Analysis of Underwater DAS Data

/in /by

A 100-meter-long fiber optic cable was installed at the bottom of a water tank at the Davidson Laboratory, together with a hydrophone for reference. The water tank is approximately 2.5 meters deep and 95 meters long; the tank also employs a 6-paddle wavemaker which can generate programmable surface waves. A 155-cm-long model boat weighing 6.5 kilograms was automatically dragged on the surface of the tank via an electrical towing mechanism. The movement of the model boat along the fiber cable and over the hydrophone was recorded using a commercially available NEC Distributed Acoustic Sensing (DAS) system and simultaneously by a hydrophone. The experiments were repeated with and without the artificially generated surface waves. The data obtained from the hydrophone and the DAS system are presented and compared. The results show the compatibility between the DAS data and the hydrophone data. More importantly, ourresults show that it is possible to measure the surface waves and to detect a surface vessel approaching the sensor by only using the time domain analysis in terms of detected total energy over time.

Read more

Optical Flow Processing for Chirp-Pulse Coherent OTDR

/in /by

We propose a novel optical flow processing technique for distributed temperature and strain sensing with the chirped-pulse coherent OTDR. Unlike conventional 1-dimensional cross-correlation methods, the technique treats the 2-dimensional waterfall data as sequential video frames, estimating local shifts through optical flow. The weighted least square approach with adaptive window size enables pixel-level optical flow calculation, providing accurate local shifts via accumulative tracks with enhanced spatial resolution. Preliminary experimental results over 20km fiber demonstrate its effectiveness for dynamic temperature and strain sensing, addressing limitations of traditional methods and improving sensing capabilities.

Read more

Multiple Sensor-head Phase-sensitive Optical Time-domain Laser Vibrometer

/in /by

We propose a hybrid remote and distributed vibration sensing system based on phase-sensitive optical time-domain reflectometry with collimator-based sensor heads. We demonstrate dual-laser vibrometers that detects nm-scale displacements of remote targets.

Read more

CLAP-S: Support Set Based Adaptation for Downstream Fiber-optic Acoustic Recognition

/in /by

Contrastive Language-Audio Pretraining (CLAP) models have demonstrated unprecedented performance in various acoustic signal recognition tasks. Fiber-optic-based acoustic recognition is one of the most important downstream tasks and plays a significant role in environmental sensing. Adapting CLAP for fiber-optic acoustic recognition has become an active research area. As a non-conventional acoustic sensor, fiberoptic acoustic recognition presents a challenging, domain-specific, low-shot deployment environment with significant domain shifts due to unique frequency response and noise characteristics. To address these challenges, we propose a support-based adaptation method, CLAP-S, which linearly interpolates a CLAP Adapter with the Support Set, leveraging both implicit knowledge through fine-tuning and explicit knowledge retrieved from memory for cross-domain generalization. Experimental results show that our method delivers competitive performance on both laboratory recorded fiber-optic ESC-50 datasets and a real-world fiber optic gunshot-firework dataset. Our research also provides valuable insights for other downstream acoustic recognition tasks.

Read more

Text-guided Device-realistic Sound Generation for Fiber-based Sound Event Classification

/in /by

Recent advancements in unique acoustic sensing devices and large-scale audio recognition models have unlocked new possibilities for environmental sound monitoring and detection. However, applying pretrained models to non-conventional acoustic sensors results in performance degradation due to domain shifts, caused by differences in frequency response and noise characteristics from the original training data. In this study, we introduce a text-guided framework for generating new datasets to retrain models specifically for these non-conventional sensors efficiently. Our approach integrates text-conditional audio generative models with two additional steps: (1) selecting audio samples based on text input to match the desired sounds, and (2) applying domain transfer techniques using recorded impulse responses and background noise to simulate the characteristics of the sensors. We demonstrate this process by generating emulated signals for fiber-optic Distributed Acoustic Sensors (DAS), creating datasets similar to the recorded ESC-50 dataset. The generated signals are then used to train a classifier, which outperforms few-shot learning approaches in environmental sound classification.

Read more

Trainingless Adaptation of Pretrained Models for Environmental Sound Classification

/in /by

Deep neural network (DNN)-based models for environmental sound classification are not robust against a domain to which training data do not belong, that is, out-of-distribution or unseen data. To utilize pretrained models for the unseen domain, adaptation methods, such as finetuning and transfer learning, are used with rich computing resources, e.g., the graphical processing unit (GPU). However, it is becoming more difficult to keep up with research trends for those who have poor computing resources because state-of-the-art models are becoming computationally resource-intensive. In this paper, we propose a trainingless adaptation method for pretrained models for environmental sound classification. To introduce the trainingless adaptation method, we first propose an operation of recovering time–frequency-ish (TF-ish) structures in intermediate layers of DNN models. We then propose the trainingless frequency filtering method for domain adaptation, which is not a gradient-based optimization widely used. The experiments conducted using the ESC-50 dataset show that the proposed adaptation method improves the classification accuracy by 20.40 percentage points compared with the conventional method.

Read more

1.2 Tb/s/l Real Time Mode Division Multiplexing Free Space Optical Communication with Commercial 400G Open and Disaggregated Transponders

/in /by

We experimentally demonstrate real time mode division multiplexing free space optical communication with commercial 400G open and disaggregated transponders. As proof of concept,using HG00, HG10, and HG01 modes, we transmit 1.2 Tb/s/l (3´1l´400Gb/s) error free.

Read more

DiffOptics: A Conditional Diffusion Model for Fiber Optics Sensing Data Imputation

/in /by

We present a generative AI framework based on a conditional diffusion model for distributed acoustic sensing (DAS) data imputation. The proposed DiffOptics model generates high-quality DAS data of various acoustic events using telecom fiber cables.

Read more

Dual Privacy Protection for Distributed Fiber Sensing with Disaggregated Inference and Fine-tuning of Memory-Augmented Networks

/in /by

We propose a memory-augmented model architecture with disaggregated computation infrastructure for fiber sensing event recognition. By leveraging geo-distributed computingresources in optical networks, this approach empowers end-users to customize models while ensuring dual privacy protection.

Read more

Enhancing EDFAs Greybox Modeling in Optical Multiplex Sections Using Few-Shot Learning

/in /by

We combine few-shot learning and grey-box modeling for EDFAs in optical lines, training a single EDFA model on 500 spectral loads and transferring it to other EDFAs using 4-8 samples, maintaining low OSNR prediction error.

Read more