TY - GEN
T1 - A Full-time-utilization Optimization Approach for Computational Nanobiosensing
AU - Shi, Shaolong
AU - Wang, Zhijing
AU - Chen, Yifan
AU - Liu, Qiang
AU - Zhang, Qingfu
PY - 2022
Y1 - 2022
N2 - We have proposed a novel framework of computational nanobiosensing (CONA) by transforming the early tumor detection process into an in vivo function optimization problem. The tumor is viewed as the global optimal solution, which is to be searched for by the nanorobots that act as the computational agents. The external control and tracking devices are used to perform actuation and imaging of nanorobots, which correspond to the optimization and learning processes of CONA, respectively. Our previous investigations are based on the protocol that the nanorobot control and tracking are alternating processes, which means that the optimization procedure does not fully utilize the available time for computing. Considering that the parallel execution of control and tracking is a superior way to realize real-time steering of nanorobots, we propose a full-time-utilization (FTU) optimization approach in this paper to improve the performance of CONA. Some in silico experiments are carried out in a 3D search space to demonstrate the superiority of the proposed FTU optimization approach compared to the previous partial-time-utilization (PTU) one.
AB - We have proposed a novel framework of computational nanobiosensing (CONA) by transforming the early tumor detection process into an in vivo function optimization problem. The tumor is viewed as the global optimal solution, which is to be searched for by the nanorobots that act as the computational agents. The external control and tracking devices are used to perform actuation and imaging of nanorobots, which correspond to the optimization and learning processes of CONA, respectively. Our previous investigations are based on the protocol that the nanorobot control and tracking are alternating processes, which means that the optimization procedure does not fully utilize the available time for computing. Considering that the parallel execution of control and tracking is a superior way to realize real-time steering of nanorobots, we propose a full-time-utilization (FTU) optimization approach in this paper to improve the performance of CONA. Some in silico experiments are carried out in a 3D search space to demonstrate the superiority of the proposed FTU optimization approach compared to the previous partial-time-utilization (PTU) one.
KW - computational nanobiosensing
KW - in vivo optimization
KW - nanorobots
KW - real-time steering
KW - tumor detection
UR - http://www.scopus.com/inward/record.url?scp=85142920974&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85142920974&origin=recordpage
U2 - 10.1109/NANO54668.2022.9928588
DO - 10.1109/NANO54668.2022.9928588
M3 - RGC 32 - Refereed conference paper (with host publication)
T3 - Proceedings of the IEEE Conference on Nanotechnology
SP - 555
EP - 558
BT - 2022 IEEE 22nd International Conference on Nanotechnology (NANO)
PB - IEEE
T2 - 22nd IEEE International Conference on Nanotechnology, NANO 2022
Y2 - 4 July 2022 through 8 July 2022
ER -