A Magnetic Manipulation System Using an Active Filter for Electronic Detection of Target Cells

Abstract

Important advances in the development of magnetic manipulation devices have been recorded over the last few years and promising experimental results have been presented. In this article we first perform a detailed analysis on one of most widely used magnetic actuators, namely a planar microcoil. Key parameters that affect the performance of the actuator are identified and our results are in accordance with measured data. Making use of these findings, a lab-on-a-chip system is proposed, that also integrates a novel electronic sensing concept for live monitoring of its activity. Possible applications of this system include highly selective bioseparation or the possibility to manipulate and assemble marked particles with great precision.

Large sampling intervals for learning and predicting chaotic systems with reservoir computing

“…Another approach is data-based prediction, which uses machine learning to construct a model from past measurements of the system state evolution (training data) [8]. In decades, methods such as recurrent neural networks (RNNs), multi-layer perceptrons, and support vector machines in machine learning have been continuously developed and applied in the study of the prediction of dynamical systems [9,10]. For example, multi-layer perception has been used in the field of functional approximation prediction, such as estimating the load of a computational system or modeling the evolution of a chemical reaction in a polymerization [9].…”

“…In decades, methods such as recurrent neural networks (RNNs), multi-layer perceptrons, and support vector machines in machine learning have been continuously developed and applied in the study of the prediction of dynamical systems [9,10]. For example, multi-layer perception has been used in the field of functional approximation prediction, such as estimating the load of a computational system or modeling the evolution of a chemical reaction in a polymerization [9]. RNN is mainly used to process sequence data, such as speech recognition, and is widely used in the natural language processing field [10].…”