TY - GEN
T1 - A tempo-spatial compressed sensing architecture for efficient high-throughput information acquisition in organs-on-a-chip
AU - Song, Chen
AU - Wang, Aosen
AU - Lin, Feng
AU - Zhao, Ruogang
AU - Jin, Zhanpeng
AU - Xu, Wenyao
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/4/11
Y1 - 2017/4/11
N2 - As a micro engineered biomimetic system to replicate key functions of living organs, organs-on-a-chip (OC) technology provides a high-throughput model for investigating complex cell interactions with a high temporal and spatial resolution in biological studies. Typically, microscopy and highspeed video cameras are used for data acquisition, which are expensive and bulky. Recently, compressed sensing (CS) has increasingly attracted attentions due to its extremely low-complexity structure and low sampling rate. However, there is no CS solution tailored for tempo-spatial information acquisition. In this paper, we propose Tempo-Spatial CS (TS-CS), a unified CS architecture for OC stream which achieves significant cost reduction and truly combines sensing with compression along the temporal and spatial domains. We point out that TS-CS can consistently achieve better performance by exploiting tempo-spatial compressibility in OC data. To this end, we present TS-CS architecture and comprehensively evaluate the system performance. With comparison to the traditional way, we show that TS-CS always obtains better recovery result with a throughput bound and can achieve around 25% throughput improvement under a reconstruction demand.
AB - As a micro engineered biomimetic system to replicate key functions of living organs, organs-on-a-chip (OC) technology provides a high-throughput model for investigating complex cell interactions with a high temporal and spatial resolution in biological studies. Typically, microscopy and highspeed video cameras are used for data acquisition, which are expensive and bulky. Recently, compressed sensing (CS) has increasingly attracted attentions due to its extremely low-complexity structure and low sampling rate. However, there is no CS solution tailored for tempo-spatial information acquisition. In this paper, we propose Tempo-Spatial CS (TS-CS), a unified CS architecture for OC stream which achieves significant cost reduction and truly combines sensing with compression along the temporal and spatial domains. We point out that TS-CS can consistently achieve better performance by exploiting tempo-spatial compressibility in OC data. To this end, we present TS-CS architecture and comprehensively evaluate the system performance. With comparison to the traditional way, we show that TS-CS always obtains better recovery result with a throughput bound and can achieve around 25% throughput improvement under a reconstruction demand.
UR - https://www.scopus.com/pages/publications/85018442016
U2 - 10.1109/BHI.2017.7897247
DO - 10.1109/BHI.2017.7897247
M3 - Conference contribution
AN - SCOPUS:85018442016
T3 - 2017 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017
SP - 229
EP - 232
BT - 2017 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017
Y2 - 16 February 2017 through 19 February 2017
ER -