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Found 11 papers, 2 papers with code
RF-Net: a Unified Meta-learning Framework for RF-enabled One-shot Human Activity Recognition
Radio-Frequency (RF) based device-free Human Activity Recognition (HAR) rises as a promising solution for many applications.
OCHID-Fi: Occlusion-Robust Hand Pose Estimation in 3D via RF-Vision
To overcome the challenge in labeling RF imaging given its human incomprehensible nature, OCHID-Fi employs a cross-modality and cross-domain training process.
Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments
Labeled Faces in the Wild (LFW) database has been widely utilized as the benchmark of unconstrained face verification and due to big data driven machine learning methods, the performance on the database approaches nearly 100%.
Variational Component Decoder for Source Extraction from Nonlinear Mixture
In many practical scenarios of signal extraction from a nonlinear mixture, only one (signal) source is intended to be extracted.
SiWa: See into Walls via Deep UWB Radar
To this end, we equip SiWa with a deep learning pipeline to parse the rich sensory data.
V2iFi: in-Vehicle Vital Sign Monitoring via Compact RF Sensing
Given the significant amount of time people spend in vehicles, health issues under driving condition have become a major concern.
Enhancing RF Sensing with Deep Learning: A Layered Approach
To better understand this potential, this article takes a layered approach to summarize RF sensing enabled by deep learning.
MoRe-Fi: Motion-robust and Fine-grained Respiration Monitoring via Deep-Learning UWB Radar
Crucial for healthcare and biomedical applications, respiration monitoring often employs wearable sensors in practice, causing inconvenience due to their direct contact with human bodies.
Adv-4-Adv: Thwarting Changing Adversarial Perturbations via Adversarial Domain Adaptation
Whereas adversarial training can be useful against specific adversarial perturbations, they have also proven ineffective in generalizing towards attacks deviating from those used for training.
AutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Driving
Object detection with on-board sensors (e. g., lidar, radar, and camera) play a crucial role in autonomous driving (AD), and these sensors complement each other in modalities.
HoloFed: Environment-Adaptive Positioning via Multi-band Reconfigurable Holographic Surfaces and Federated Learning
Our simulation results confirm that HoloFed achieves a 57% lower positioning error variance compared to a beam-scanning baseline and can effectively adapt to diverse environments.
