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Kellman M, Rivest F, Pechacek A, et al. Node-Pore Coded Coincidence Correction: Coulter Counters, Code Design, and Sparse Deconvolution. IEEE Sens J. 2018;18(8):3068-3079doi: 10.1109/JSEN.2018.2805865.
Kellman, M., Rivest, F., Pechacek, A., Sohn, L., & Lustig, M. (2018). Node-Pore Coded Coincidence Correction: Coulter Counters, Code Design, and Sparse Deconvolution. IEEE sensors journal, 18(8), 3068-3079. https://doi.org/10.1109/JSEN.2018.2805865
Kellman, Michael, et al. "Node-Pore Coded Coincidence Correction: Coulter Counters, Code Design, and Sparse Deconvolution." IEEE sensors journal vol. 18,8 (2018): 3068-3079. doi: https://doi.org/10.1109/JSEN.2018.2805865
Kellman M, Rivest F, Pechacek A, Sohn L, Lustig M. Node-Pore Coded Coincidence Correction: Coulter Counters, Code Design, and Sparse Deconvolution. IEEE Sens J. 2018 Apr 15;18(8):3068-3079. doi: 10.1109/JSEN.2018.2805865. Epub 2018 Feb 14. PMID: 29988953; PMCID: PMC6034687.
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IEEE Sens J. 2018 Apr 15;18(8):3068-3079. doi: 10.1109/JSEN.2018.2805865. Epub 2018 Feb 14.

Node-Pore Coded Coincidence Correction: Coulter Counters, Code Design, and Sparse Deconvolution.

IEEE sensors journal

Michael Kellman, Francois Rivest, Alina Pechacek, Lydia Sohn, Michael Lustig

Affiliations

  1. Dept. of Electrical Engineering and Computer Sciences, University of California, Berkeley.
  2. Dept. of Mechanical Engineering, University of California, Berkeley.
  3. Institute of Bioengineering, Ecole Polytechnique Federale de Lausanne, Switzerland.
  4. Graduate Program in Bioengineering, University of California Berkeley, Berkeley, CA, USA.

PMID: 29988953 PMCID: PMC6034687 DOI: 10.1109/JSEN.2018.2805865

Abstract

We present a novel method to perform individual particle (e.g. cells or viruses) coincidence correction through joint channel design and algorithmic methods. Inspired by multiple-user communication theory, we modulate the channel response, with Node-Pore Sensing, to give each particle a binary Barker code signature. When processed with our modified successive interference cancellation method, this signature enables both the separation of coincidence particles and a high sensitivity to small particles. We identify several sources of modeling error and mitigate most effects using a data-driven self-calibration step and robust regression. Additionally, we provide simulation analysis to highlight our robustness, as well as our limitations, to these sources of stochastic system model error. Finally, we conduct experimental validation of our techniques using several encoded devices to screen a heterogeneous sample of several size particles.

Keywords: Barker Codes; Coincidence Correction; Computational Sensing; Coulter Counter; Inverse Problems; Node-Pore Sensing; Successive Interference Cancellation

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