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Found 8 papers, 3 papers with code
TelecomTM: A Fine-Grained and Ubiquitous Traffic Monitoring System Using Pre-Existing Telecommunication Fiber-Optic Cables as Sensors
Our approach uses the spatial dependency of multiple virtual sensors and Newton's laws of motion to combine the distributed sensor data to reduce uncertainties in vehicle detection and tracking.
GaitVibe+: Enhancing Structural Vibration-based Footstep Localization Using Temporary Cameras for In-home Gait Analysis
In the fusion stage, both cameras and vibration sensors are installed to record only a few trials of the subject's footstep data, through which we characterize the uncertainty in wave arrival time and model the wave velocity profiles for the given structure.
Spatial Deep Deconvolution U-Net for Traffic Analyses with Distributed Acoustic Sensing
In response, we introduce a self-supervised U-Net model that can suppress background noise and compress car-induced DAS signals into high-resolution pulses through spatial deconvolution.
A hierarchical semantic segmentation framework for computer vision-based bridge damage detection
However, existing methods perform poorly when detecting small damages (e. g., cracks and exposed rebars) and thin objects with limited image samples, especially when the components of interest are highly imbalanced.
Vibration-Based Bridge Health Monitoring using Telecommunication Cables
Bridge Health Monitoring (BHM) enables early damage detection of bridges and is thus critical for avoiding more severe damages that might result in major financial and human losses.
HierMUD: Hierarchical Multi-task Unsupervised Domain Adaptation between Bridges for Drive-by Damage Diagnosis
Monitoring bridge health using vibrations of drive-by vehicles has various benefits, such as no need for directly installing and maintaining sensors on the bridge.
Knowledge transfer between bridges for drive-by monitoring using adversarial and multi-task learning
Specifically, we train a deep network in an adversarial way to learn features that are 1) sensitive to damage and 2) invariant to different bridges.
Damage-sensitive and domain-invariant feature extraction for vehicle-vibration-based bridge health monitoring
We introduce a physics-guided signal processing approach to extract a damage-sensitive and domain-invariant (DS & DI) feature from acceleration response data of a vehicle traveling over a bridge to assess bridge health.
