890 citations · 3.4k across the 17 of their papers we have counts for
7 papers · 1 filter
Bayesian De-quantization and Data Compression for Low-Energy Physiological Signal Telemonitoring
Benyuan Liu, Hongqi Fan, Qiang Fu +1
We address the issue of applying quantized compressed sensing (CS) on low-energy telemonitoring. So far, few works studied this problem in applications where signals were only appr…
Spatiotemporal Sparse Bayesian Learning with Applications to Compressed Sensing of Multichannel Physiological Signals
Zhilin Zhang, Tzyy-Ping Jung, Scott Makeig +2
Energy consumption is an important issue in continuous wireless telemonitoring of physiological signals. Compressed sensing (CS) is a promising framework to address it, due to its…
Compressed Sensing for Energy-Efficient Wireless Telemonitoring: Challenges and Opportunities
Zhilin Zhang, Bhaskar D. Rao, Tzyy-Ping Jung
As a lossy compression framework, compressed sensing has drawn much attention in wireless telemonitoring of biosignals due to its ability to reduce energy consumption and make poss…
Energy Efficient Telemonitoring of Physiological Signals via Compressed Sensing: A Fast Algorithm and Power Consumption Evaluation
Benyuan Liu, Zhilin Zhang, Gary Xu +2
Wireless telemonitoring of physiological signals is an important topic in eHealth. In order to reduce on-chip energy consumption and extend sensor life, recorded signals are usuall…
Compression via Compressive Sensing : A Low-Power Framework for the Telemonitoring of Multi-Channel Physiological Signals
Benyuan Liu, Zhilin Zhang, Hongqi Fan +1
Telehealth and wearable equipment can deliver personal healthcare and necessary treatment remotely. One major challenge is transmitting large amount of biosignals through wireless…
Fast Marginalized Block Sparse Bayesian Learning Algorithm
Benyuan Liu, Zhilin Zhang, Hongqi Fan +1
The performance of sparse signal recovery from noise corrupted, underdetermined measurements can be improved if both sparsity and correlation structure of signals are exploited. On…