A Tempo-Spatial Compressed Sensing Architecture for Efficient High-Throughput Information Acquisition in Organs-on-a-Chip

Chen Song1, Aosen Wang2, Feng Lin1, Ruogang Zhao3, Zhanpeng Jin, Wenyao Xu1

  • 1State University of New York, Buffalo
  • 2University at Buffalo
  • 3University at Buffalo, State University of New York



Contributed Paper (Oral)


3. Sensor Informatics


14:30 - 16:00 | Fri 17 Feb | Salon 5 | FrB1

Fr1.2: Sensor Informatics (Monitoring/Architecture)


As a microengineered 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 (TSCS), 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%.

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