Direct Current Electrokinetic Particle Transport in Micro/Nano-Fluidics

Electrokinetics has been widely used to propel and manipulate particles in micro/nano-fluidics. The first part of this dissertation focuses on numerical and experimental studies of direct current (DC) electrokinetic particle transport in microfluidics, with emphasis on dielectrophoretic (DEP) effect. Especially, the electrokinetic transports of spherical particles in a converging-diverging microchannel and an L-shaped microchannel, and cylindrical algal cells in a straight microchannel have been numerically and experimentally studied. The numerical predictions are in quantitative agreement with our own and other researchers\u27 experimental results. It has been demonstrated that the DC DEP effect, neglected in existing numerical models, plays an important role in the electrokinetic particle transport and must be taken into account in the numerical modeling. The induced DEP effect could be utilized in microfluidic devices to separate, focus and trap particles in a continuous flow, and align non-spherical particles with their longest axis parallel to the applied electric field. The DEP particle-particle interaction always tends to chain and align particles parallel to the applied electric field, independent of the initial particle orientation except an unstable orientation perpendicular to the electric field imposed. The second part of this dissertation for the first time develops a continuum-based numerical model, which is capable of dynamically tracking the particle translocation through a nanopore with a full consideration of the electrical double layers (EDLs) formed adjacent to the charged particles and nanopores. The predictions on the ionic current change due to the presence of particles inside the nanopore are in qualitative agreement with molecular dynamics simulations and existing experimental results. It has been found that the initial orientation of the particle plays an important role in the particle translocation and also the ionic current through the nanopore. Furthermore, field effect control of DNA translocation through a nanopore using a gate electrode coated on the outer surface of the nanopore has been numerically demonstrated. This technique offers a more flexible and electrically compatible approach to regulate the DNA translocation through a nanopore for DNA sequencing

VisuaLizations As Intermediate Representations (VLAIR) : an approach for applying deep learning-based computer vision to non-image-based data

We thank the China Scholarship Council (CSC) for financially supporting my PhD study at University of St Andrews, UK, and NSERC Discovery Grant 2020-04401 (Miguel Nacenta).Deep learning algorithms increasingly support automated systems in areas such as human activity recognition and purchase recommendation. We identify a current trend in which data is transformed first into abstract visualizations and then processed by a computer vision deep learning pipeline. We call this VisuaLization As Intermediate Representation (VLAIR) and believe that it can be instrumental to support accurate recognition in a number of fields while also enhancing humans’ ability to interpret deep learning models for debugging purposes or in personal use. In this paper we describe the potential advantages of this approach and explore various visualization mappings and deep learning architectures. We evaluate several VLAIR alternatives for a specific problem (human activity recognition in an apartment) and show that VLAIR attains classification accuracy above classical machine learning algorithms and several other non-image-based deep learning algorithms with several data representations.Publisher PDFPeer reviewe

PATH AND DIRECTION DISCOVERY IN INDIVIDUAL DYNAMIC MODELS: A REGULARIZED HYBRID UNIFIED STRUCTURAL EQUATION MODELING WITH LATENT VARIABLE

There recently has been growing interest in the study of psychological and neurologicalprocesses at an individual level. One goal in such endeavors is to construct person-specific dynamicassessments using time series techniques such as Vector Autoregressive (VAR) models. Within thepsychometric field, researchers have developed psychometric modeling frameworks to estimatedifferent variants of VAR models. These modeling frameworks estimate the dynamic relations (e.g.,temporal and contemporaneous) unpacked in a multivariate time series data. However, two problemsexist with current VAR specifications: 1) VAR models are restricted in that contemporaneousrelations are typically modeled either as undirected relations among residuals or directed relationsamong observed variables, but not both; 2) current estimation frameworks are limited by thereliance on stepwise model building procedures. This study adopts a new modeling approach, i.e.,LASSO regularized hybrid unified Structural Equation Model (SEM), for a global search andestimation of a more flexible VAR representation. The present study to our knowledge is the firstapplication of the recently developed regularized SEM technique to the estimation of a type of timeseries SEM, which points to a promising future for statistical learning in psychometric models.Doctor of Philosoph

Can We Distinguish Between Different Longitudinal Models for Estimating Nonlinear Trajectories

Substantive theory rarely provides specific enough information to guide our selection of the optimal model for longitudinal data. Instead, researchers are more likely to rely on models common to their field, even if it is not appropriate. The purpose of our study is to assess whether researchers can use overall goodness of fit measures from structural equation models to correctly find the data generating model (DGM) from among a broad set of different longitudinal models. I use four different DGM adapted from published empirical studies. I compare goodness-of-fit statistics (e.g., p-value, CFI, RMSEA, etc.) of the DGM with those of six alternative models. Overall, the BIC performed best in selecting the DGM, though no fit statistic was flawless. In the absence of substantive theory, I recommend that researchers begin with the most general longitudinal model and test whether it can be simplified by eliminating parameters.Master of Art

Explicit Estimation of Magnitude and Phase Spectra in Parallel for High-Quality Speech Enhancement

Phase information has a significant impact on speech perceptual quality and intelligibility. However, existing speech enhancement methods encounter limitations in explicit phase estimation due to the non-structural nature and wrapping characteristics of the phase, leading to a bottleneck in enhanced speech quality. To overcome the above issue, in this paper, we proposed MP-SENet, a novel Speech Enhancement Network which explicitly enhances Magnitude and Phase spectra in parallel. The proposed MP-SENet adopts a codec architecture in which the encoder and decoder are bridged by time-frequency Transformers along both time and frequency dimensions. The encoder aims to encode time-frequency representations derived from the input distorted magnitude and phase spectra. The decoder comprises dual-stream magnitude and phase decoders, directly enhancing magnitude and wrapped phase spectra by incorporating a magnitude estimation architecture and a phase parallel estimation architecture, respectively. To train the MP-SENet model effectively, we define multi-level loss functions, including mean square error and perceptual metric loss of magnitude spectra, anti-wrapping loss of phase spectra, as well as mean square error and consistency loss of short-time complex spectra. Experimental results demonstrate that our proposed MP-SENet excels in high-quality speech enhancement across multiple tasks, including speech denoising, dereverberation, and bandwidth extension. Compared to existing phase-aware speech enhancement methods, it successfully avoids the bidirectional compensation effect between the magnitude and phase, leading to a better harmonic restoration. Notably, for the speech denoising task, the MP-SENet yields a state-of-the-art performance with a PESQ of 3.60 on the public VoiceBank+DEMAND dataset.Comment: Submmited to IEEE Transactions on Audio, Speech and Language Processin