TY - GEN
T1 - Analog Nanobiosensing of Continuous Disease State
T2 - 22nd IEEE International Conference on Nanotechnology, NANO 2022
AU - Chen, Yifan
AU - Wu, Mengfei
AU - Shi, Shaolong
PY - 2022
Y1 - 2022
N2 - We propose a new framework of analog nanobiosensing (ANNA), which refers to a nanobiosensing process that produces an output “analogous” to the underlying continuous disease state (DS). This is in contrast to the traditional discrete nanobiosensing where the number of DSs is finite, effectively leading to the act of classifying. We present a mathematical model of ANNA including a generic representation of the continuous DS that converts the linguistic variable of DS \mathrm{S} into a normalized numerical variable s , a surjective sensing function \mathcal{F} that maps one set defined on universe \mathbb{S} (i.e., collection of all s) to another set defined on universe \mathbb{O} (i.e., collection of all sensing outputs o ), and a generic sensing performance measure that is dependent on the inverse image of o under function \mathcal{F} . Furthermore, we analyze the framework from a fuzzy-set-theoretic perspective, which provides additional insight into the design and optimization of ANNA.
AB - We propose a new framework of analog nanobiosensing (ANNA), which refers to a nanobiosensing process that produces an output “analogous” to the underlying continuous disease state (DS). This is in contrast to the traditional discrete nanobiosensing where the number of DSs is finite, effectively leading to the act of classifying. We present a mathematical model of ANNA including a generic representation of the continuous DS that converts the linguistic variable of DS \mathrm{S} into a normalized numerical variable s , a surjective sensing function \mathcal{F} that maps one set defined on universe \mathbb{S} (i.e., collection of all s) to another set defined on universe \mathbb{O} (i.e., collection of all sensing outputs o ), and a generic sensing performance measure that is dependent on the inverse image of o under function \mathcal{F} . Furthermore, we analyze the framework from a fuzzy-set-theoretic perspective, which provides additional insight into the design and optimization of ANNA.
KW - analog nanobiosensing
KW - computational nanobiosensing
KW - Continuous health state
KW - fuzzy set theory
UR - http://www.scopus.com/inward/record.url?scp=85142923715&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85142923715&origin=recordpage
U2 - 10.1109/NANO54668.2022.9928732
DO - 10.1109/NANO54668.2022.9928732
M3 - RGC 32 - Refereed conference paper (with host publication)
T3 - Proceedings of the IEEE Conference on Nanotechnology
SP - 551
EP - 554
BT - 2022 IEEE 22nd International Conference on Nanotechnology (NANO)
PB - IEEE
Y2 - 4 July 2022 through 8 July 2022
ER -