Showing papers by "Peter C. Y. Chen published in 2012"

Real-time control of a microfluidic channel for size-independent deformability cytometry

Abstract: Mechanical properties of cells can be correlated with various cell states and are now considered as an important class of biophysical markers Effectiveness of existing high-throughput microfluidic techniques for investigating cell mechanical properties is adversely affected by cell-size variation in a given cell population In this work, we introduce a new microfluidic system with real-time feedback control to evaluate single-cell deformability while minimizing cell-size dependence of the measurement Using breast cancer cells (MCF-7), we demonstrate the potential of this system for stiffness profiling of cells in complex, diverse cell populations

Analysis of Stiffness in Extracellular Matrix Embedded with Bio-Conjugated Magnetic Beads in a Magnetic Field

Abstract: In this paper we present a new model for determining the local stiffness of an extracellular matrix (ECM) sample embedded with bio-conjugated magnetic beads under the influence of an external magnetic field In this model, the viscoelastic deformation of such ECM samples is analyzed using the finite element method We report results from numerical simulations using our model on two typical scenarios for studying the pre-tension in the ECM caused by beads under a magnetic field The analytical results are in close agreement with that obtained from COMSOL We also applied our model on an actual ECM sample embedded with bio-conjugated beads and compared our analytical results with that obtained from stretch tests done on that sample These results are comparable to that from the stretching tests In this paper, we present a finite-element model for determining the local stiffness of an ECM sample embedded with bio-conjugated beads under the influence of an external magnetic field Section II discusses the modification of ECM for manipulating the stiffness of ECM Section III describes our finite-element method for analyzing viscoelastic deformation of ECM gel embedded with bio-conjugated beads, while Section IV discusses the calculation of magnetic force induced on the beads in the magnetic field generated by a permanent magnet In Section V we apply this finite-element model to simulate the pre-tension generated in ECM by (i) a single bead, and (ii) two columns of aligned beads, and compare the results to that obtained using COMSOL We also apply our finite-element model to an ECM sample with randomly distributed beads, and compare the analytical results (in terms of the percentage change in ECM stiffness) with experimental results obtained from a stretch test done on an actual ECM sample Simulations are also conducted to reveal the influence of the size and concentration of beads on the change in ECM stiffness We discuss possible improvements for this proposed model in Section VI

An application of narx-network to reduce contour errors

Abstract: The problem of improving the contour error in twodimensional CNC machines is considered in this paper. The nonlinear autoregressive with exogenous inputs (NARX) network is a dynamic neural architecture commonly used for input-output modeling of nonlinear dynamic systems. In the work presented here, two sets of off-line trained NARX networks are used to predict the position outputs at the next sampling time instant for the two axes of a CNC machine. From these values, the expected axial components of the contour error for the next instant is computed and used to adjust the reference position inputs to compensate for this error. The inputs to the NARX networks are the original uncompensated reference position inputs and actual axial positions together with corresponding values in past instances, the number of these latter depending upon the complexity of the dynamics of the system. An iterative procedure is used to improve compensation performance. Simulation results using linear, circular and parabolic contours show that this approach can significantly improve contouring accuracy. Although modelbased in its control strategy, this approach does not require an accurate knowledge of the system’s dynamic model as the NARX networks are trained using actual input-output data which can be readily obtained from the system during operation.