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1. Avoiding RF Isolators: Reflectionless Microwave Bandpass Filtering Components for Advanced RF Front Ends

   https://doi.org/10.1109/MMM.2020.3023222  

   Gomez-Garcia, Roberto ; Psychogiou, Dimitra ; Munoz-Ferreras, Jose-Maria ; Yang, Li ( December 2020 , IEEE Microwave Magazine)

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2. Teaching RF to Sense without RF Training Measurements

   https://doi.org/10.1145/3432224  

   Cai, Hong ; Korany, Belal ; Karanam, Chitra R. ; Mostofi, Yasamin ( December 2020 , Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies)

   In this paper, we propose a novel, generalizable, and scalable idea that eliminates the need for collecting Radio Frequency (RF) measurements, when training RF sensing systems for human-motion-related activities. Existing learning-based RF sensing systems require collecting massive RF training data, which depends heavily on the particular sensing setup/involved activities. Thus, new data needs to be collected when the setup/activities change, significantly limiting the practical deployment of RF sensing systems. On the other hand, recent years have seen a growing, massive number of online videos involving various human activities/motions. In this paper, we propose to translate such already-available online videos to instant simulated RF data for training any human-motion-based RF sensing system, in any given setup. To validate our proposed framework, we conduct a case study of gym activity classification, where CSI magnitude measurements of three WiFi links are used to classify a person's activity from 10 different physical exercises. We utilize YouTube gym activity videos and translate them to RF by simulating the WiFi signals that would have been measured if the person in the video was performing the activity near the transceivers. We then train a classifier on the simulated data, and extensively test it with real WiFi data of 10 subjects performing the activities in 3 areas. Our system achieves a classification accuracy of 86% on activity periods, each containing an average of 5.1 exercise repetitions, and 81% on individual repetitions of the exercises. This demonstrates that our approach can generate reliable RF training data from already-available videos, and can successfully train an RF sensing system without any real RF measurements. The proposed pipeline can also be used beyond training and for analysis and design of RF sensing systems, without the need for massive RF data collection. 

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3. RF-Q: Unsupervised Signal Quality Assessment for Robust RF-based Respiration Monitoring

   Xie, Zongxing ; Nederlander, Ava ; Park, Isac ; Ye, Fan ( January 2023 , ACM/IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies)

   Continuous monitoring of respiration provides invaluable insights about health status management (e.g., the progression or recovery of diseases). Recent advancements in radio frequency (RF) technologies show promise for continuous respiration monitoring by virtue of their non-invasive nature, and preferred over wearable solutions that require frequent charging and continuous wearing. However, RF signals are susceptible to large body movements, which are inevitable in real life, challenging the robustness of respiration monitoring. While many existing methods have been proposed to achieve robust RF-based respiration monitoring, their reliance on supervised data limits their potential for broad applicability. In this context, we propose, RF-Q, an unsupervised/self-supervised model to achieve signal quality assessment and quality-aware estimation for robust RF-based respiration monitoring. RF-Q uses the recon- struction error of an autoencoder (AE) neural network to quantify the quality of respiratory information in RF signals without the need for data labeling. With the combination of the quantified sig- nal quality and reconstructed signal in a weighted fusion, we are able to achieve improved robustness of RF respiration monitor- ing. We demonstrate that, instead of applying sophisticated models devised with respective expertise using a considerable amount of labeled data, by just quantifying the signal quality in an unsupervised manner we can significantly boost the average end-to-end (e2e) respiratory rate estimation accuracy of a baseline by an improvement ratio of 2.75, higher than the gain of 1.94 achieved by a supervised baseline method that excludes distorted data. 

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4. RF-Q: Unsupervised Signal Quality Assessment for Robust RF-based Respiration Monitoring

   Xie, Zongxing ; Nederlander, Ava ; Park, Isac ; Ye, Fan ( January 2023 , ACM/IEEE International Conference on Connected Health: Applications, Systemsand Engineering Technologies (CHASE))
5. RF Fingerprint Classification With Combinatorial-Randomness-Based Power Amplifiers and Convolutional Neural Networks: Secure analog/RF electronics and electromagnetics

   Chen, Vanessa ; Xu, Jiachen ; Shen, Yuyi ; Chen, Ethan ( November 2022 , IEEE solidstate circuits magazine)