NEC Laboratories America

Optical Sensing & Solutions

From early concepts and laboratory validation to proven field deployments on live infrastructure, we develop technologies that sense the physical world, transform observations into intelligence, and enable informed action. Building on our expertise in optics and photonics, we are expanding toward multimodal sensing, AI-powered understanding, and intelligent systems that interact with the physical world.

Deployed telecom cables, urban fiber networks, and emerging optical sensors provide a powerful foundation for observing the physical world at scale. Our research spans forward-looking theoretical studies, cutting-edge experiments, artificial intelligence, and world- and industry-first field trials. We transform existing infrastructure into dense, real-time sensing platforms capable of monitoring complex environments over large areas.

Our research follows a broad sense–understand–act vision. We develop optical and distributed fiber sensing technologies that capture physical phenomena; AI methods that interpret complex signals, combine information across sensors, diagnose events, and predict changing conditions; and intelligent solutions that translate these insights into decisions and responses. Looking ahead, we envision extending this foundation beyond optical sensing to multimodal sensing and autonomous systems, including robotics, that can perceive, reason about, and respond to the physical world.

Our current work spans optical sensing, infrared imaging and spectroscopy, radio-frequency spectrum sensing and AI-enabled sensing analytics. These technologies can detect, localize, and characterize vibrations, intrusions, equipment conditions, and environmental changes along an infrastructure network’s physical route. By integrating sensing with edge computing and AI, we seek to move beyond data collection toward real-time understanding, prediction, and automated response.

By uniting advanced sensing, physical-world AI, and intelligent action, Optical Sensing & Solutions aims to create safer, more resilient, and more autonomous systems for infrastructure, utilities, transportation, environmental monitoring, public safety, smart cities, and intelligent manufacturing.

Join Us

Are you interested in joining us? We seek the next generation of thought leaders in optical sensing for researcher positions. Outstanding applicants pursuing a career in optical sensing are encouraged to visit our careers page to learn more and apply for available positions.

Summer 2026 Internship Applications are now closed.

Exciting internship opportunities for Summer 2027 will be available on our internship page this fall.  We are looking for students pursuing advanced degrees in Computer Science and Electrical Engineering. Internships are typically 3 months long during the summer. The benefits of working for us include the opportunity to quickly join a project team applying cutting-edge technology to industry-leading concepts.

To apply for a Summer Internship, visit our Internship page.

Learn More

Want to learn more about our optical sensing & solutions department? Meet our team of experts, check out our blog posts, or read our latest publications.

Featured Optical Sensing & Solutions Research Projects

Latest Optical Sensing & Solutions News

Field Verification of Fault Localization with Integrated Physical-Parameter-Aware Methodology

We report the first field verification of fault localization in an optical line system (OLS) by integrating digital longitudinal monitoring and OLS calibration, highlighting changes in physical metrics and parameters. Use cases shown are degradation of a fiber span loss and optical amplifier noise figure.

Optical orbital angular momentum analogy to the Stern-Gerlach experiment

Symmetry breaking has been shown to reveal interesting phenomena in physical systems. A notable example is the fundamental work of Otto Stern and Walther Gerlach [Stern and Zerlach, Z. Physik 9, 349 (1922)] nearly 100 years ago demonstrating a spin angular momentum (SAM) deflection that differed from

Accelerating Distributed Machine Learning with an Efficient AllReduce Routing Strategy

We propose an efficient routing strategy for AllReduce transfers, which compromise of the dominant traffic in machine learning-centric datacenters, to achieve fast parameter synchronization in distributed machine learning, improving the average training time by 9%.

Extension of the Local-Optimization Global-Optimization (LOGO) Launch Power Strategy to Multi-Band Optical Networks

We propose extending the LOGO strategy for launch power settings to multi-band scenarios, maintaining low complexity while addressing key inter-band nonlinear effects and accurate amplifier models. This methodology simplifies multi-band optical multiplex section control, providing an immediate, descriptive

First Field Demonstration of Hollow-Core Fibre Supporting Distributed Acoustic Sensing and DWDM Transmission

We demonstrate a method for measuring the backscatter coefficient of hollow-core fibre (HCF), and show the feasibility of distributed acoustic sensing (DAS) with simultaneous 9.6-Tb/s DWDM transmission over a 1.6-km field-deployed HCF cable.

Machine Learning Model for EDFA Predicting SHB Effects

Experiments show that machine learning model of an EDFA is capable of modelling spectral hole burning effects accurately. As a result, it significantly outperforms black-box models that neglect inhomogeneous effects. Model achieves a record average RMSE of 0.0165 dB between the model predictions and