Michael Kellman

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2017-213

December 14, 2017

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2017/EECS-2017-213.pdf

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.

Advisors: Michael Lustig and Laura Waller

---

BibTeX citation:

@mastersthesis{Kellman:EECS-2017-213,
    Author= {Kellman, Michael},
    Editor= {Lustig, Michael},
    Title= {Node-Pore Coded Coincidence Correcting Microfluidic Channel Framework: Code Design and Sparse Deconvolution},
    School= {EECS Department, University of California, Berkeley},
    Year= {2017},
    Month= {Dec},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2017/EECS-2017-213.html},
    Number= {UCB/EECS-2017-213},
    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.},
}

EndNote citation:

%0 Thesis
%A Kellman, Michael 
%E Lustig, Michael 
%T Node-Pore Coded Coincidence Correcting Microfluidic Channel Framework: Code Design and Sparse Deconvolution
%I EECS Department, University of California, Berkeley
%D 2017
%8 December 14
%@ UCB/EECS-2017-213
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2017/EECS-2017-213.html
%F Kellman:EECS-2017-213