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Ruochen Wu

dblp:257/9583 · DBLP profile ↗
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2ranked-venue papers
1as first author
1since 2021 · last 2026
0000-0003-0852-424XORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Computer networks · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer networks
1 paper
Wireless sensing and localization · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Medical and health informatics · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Wireless sensing and localization › radar signal processing
FMCW radar
1.012026
Robust Biometric Information Sensing With mmWave Radar System-on-Chip · IEEE Trans. Mob. Comput. 2026
Wireless sensing and localization
radar signal processing
1.012026
Robust Biometric Information Sensing With mmWave Radar System-on-Chip · IEEE Trans. Mob. Comput. 2026
Wireless sensing and localization
vital sign monitoring
1.012026
Robust Biometric Information Sensing With mmWave Radar System-on-Chip · IEEE Trans. Mob. Comput. 2026
Medical and health informatics › digital health
health monitoring
0.312026
Robust Biometric Information Sensing With mmWave Radar System-on-Chip · IEEE Trans. Mob. Comput. 2026

Methods — techniques the papers use, named apart from their topics

matched filter · 2.0adaptive waveform processing · 2.0
YearPublicationVenuePosition
2026 Robust Biometric Information Sensing With mmWave Radar System-on-Chip
abstract
Vital information wireless sensing is an interesting alternative to conventional techniques based on contact sensors. The evolution of radar technologies enables the proposal of cost-effective and compact radar sensors designed for diverse health monitoring, diagnostics, and Internet of Medical Things (IoMT) applications. It is possible to observe respiratory rate (RR), heart rate (HR), cardiac rhythm, blood pressure waveform (BPW), and emerging biometrics from the phase of radar echo signals. However, recognition and separation of the signals of interest from other interfering motion is a challenge, taking into account the diversity of signal patterns depending on subjects, health and observation conditions. In this context, we propose a novel wireless vital sensing system with a self-designed 120 GHz Frequency-Modulated Continuous Wave (FMCW) Radar. The developed sensor, based on a Radar System-on-Chip (RSoC), uses a real-time Repetitive Waveform Adaptive Matched Filter (RWAMF) maximizing Signal-to-Interference-plus-Noise Ratio (SINR) for reliable cardiac rhythm and BPW observations. The radar design has been optimized for vital sensing with a very short wavelength providing excellent sensitivity to micro-motion and a centimetric range and transversal resolutions to reject clutter and unwanted motions. The superior performance of our solution has been validated and evaluated experimentally on different subjects and measurement conditions with our own acquired vital signal database.
Ruochen Wu, Laura Miró, Albert Aguasca, Montse Nájar, Antoni Broquetas
IEEE Trans. Mob. Comput.1
2020 Variable neighborhood search-based methods for integrated hybrid flow shop scheduling with distribution
Shijin Wang 0002, Ruochen Wu, Feng Chu 0001, Jianbo Yu 0004
Soft Comput.2