Tingfeng Li NEC Labs America

Tingfeng Li

Researcher

Optical Networking & Sensing

Posts

Deep Learning-based Intrusion Detection and Impulsive Event Classification for Distributed Acoustic Sensing across Telecom Networks

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We introduce two pioneering applications leveraging Distributed Fiber Optic Sensing (DFOS) and Machine Learning (ML) technologies. These innovations offer substantial benefits forfortifying telecom infrastructures and public safety. By harnessing existing telecom cables, our solutions excel in perimeter intrusion detection via buried cables and impulsive event classification through aerial cables. To achieve comprehensive intrusion detection, we introduce a label encoding strategy for multitask learning and evaluate the generalization performance of the proposed approach across various domain shifts. For accurate recognition of impulsive acoustic events, we compare several standard choices of representations for raw waveform data and neural network architectures, including convolutional neural networks (ConvNets) and vision transformers (ViT).We also study the effectiveness of the built-in inductive biases under both high- and low-fidelity sensing conditions and varying amounts of labeled training data. All computations are executed locally through edge computing, ensuring real-time detection capabilities. Furthermore, our proposed system seamlessly integrates with cameras for video analytics, significantly enhancing overall situation awareness of the surrounding environment.

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Learning Transferable Reward for Query Object Localization with Policy Adaptation

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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.

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Vehicle Run-Off-Road Event Automatic Detection by Fiber Sensing Technology

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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.

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