Biomedical Engineering and Bioengineering Commons^™

Bacterial Motion And Spread In Porous Environments, Yasser Almoteri

Dissertations

Micro-swimmers are ubiquitous in nature from soil and water to mammalian bodies and even many technological processes. Common known examples are microbes such as bacteria, micro-algae and micro-plankton, cells such as spermatozoa and organisms such as nematodes. These swimmers live and have evolved in multiplex environments and complex flows in the presence of other swimmers and types, inert particles and fibers, interfaces and non-trivial confinements and more. Understanding the locomotion and interactions of these individual micro-swimmers in such impure viscous fluids is crucial to understanding the emergent dynamics of such complex systems, and to further enabling us to control and …

Gradient Free Sign Activation Zero One Loss Neural Networks For Adversarially Robust Classification, Yunzhe Xue

Dissertations

The zero-one loss function is less sensitive to outliers than convex surrogate losses such as hinge and cross-entropy. However, as a non-convex function, it has a large number of local minima, andits undifferentiable attribute makes it impossible to use backpropagation, a method widely used in training current state-of-the-art neural networks. When zero-one loss is applied to deep neural networks, the entire training process becomes challenging. On the other hand, a massive non-unique solution probably also brings different decision boundaries when optimizing zero-one loss, making it possible to fight against transferable adversarial examples, which is a common weakness in deep learning …

Towards Adversarial Robustness With 01 Lossmodels, And Novel Convolutional Neural Netsystems For Ultrasound Images, Meiyan Xie

Dissertations

This dissertation investigates adversarial robustness with 01 loss models and a novel convolutional neural net systems for vascular ultrasound images.

In the first part, the dissertation presents stochastic coordinate descent for 01 loss and its sensitivity to adversarial attacks. The study here suggests that 01 loss may be more resilient to adversarial attacks than the hinge loss and further work is required.

In the second part, this dissertation proposes sign activation network with a novel gradient-free stochastic coordinate descent algorithm and its ensembling model. The study here finds that the ensembling model gives a high minimum distortion (as measured by …

Development Of Deep Learning Neural Network For Ecological And Medical Images, Shaobo Liu

Dissertations

Deep learning in computer vision and image processing has attracted attentions from various fields including ecology and medical image. Ecologists are interested in finding an effective model structure to classify different species. Tradition deep learning model use a convolutional neural network, such as LeNet, AlexNet, VGG models, residual neural network, and inception models, are first used on classifying bee wing and butterfly datasets. However, insufficient data sample and unbalanced samples in each class have caused a poor accuracy. To make improvement the test accuracy, data augmentation and transfer learning are applied. Recently developed deep learning framework based on mathematical morphology …

Blast Shock-Wave Characterization In Experimental Shock Tubes, Sudeepto Kahali

Dissertations

Blast-induced traumatic brain injuries have affected U.S. soldiers deployed for extended periods in the gulf and Afghanistan wars. To identify the biomechanical and biochemical mechanisms of injury, critical in the identification of diagnostic and therapeutic tools, compressed gas-driven shock tubes are used by investigators to study shockwave-animal specimen interactions and its biological consequences. However, shock tubes are designed and operated in a variety of geometry with a range of process parameters, and the quality of shock wave characteristics relevant to field conditions and therefore the study of blast-induced traumatic brain injuries suffered by soldiers is affected by those conditions. Lab-to-lab …

Comparison Of Longitudinal Changes In Resting State Functional Magnetic Resonance Imaging Between Alzheimer’S And Healthy Controls, Berk Can Yilmaz

Theses

Resting State Functional Magnetic Resonance Imaging (rs-fMRI) is a technique that is widely used for analyzing brain function using different approaches and methods. This study involves rs-fMRI analysis of Blood Oxygenation Level Dependent (BOLD) signals acquired from Alzheimer’s disease (AD) Patients and Healthy Controls (HC). Each subject in the study had both functional and anatomical images with at least one rs-fMRI scan with their Anatomical (T1) scans. Previous rs-fMRI studies have demonstrated that AD shows differences in Amplitude of Low Frequency (<0.1 Hz) Fluctuations (ALFF), and Regional Homogeneity (ReHo) measures according to HCs.

The aim of the study is to investigate individual and group level differences using ReHo and mALFF related …

Model-Based Deep Siamese Autoencoder For Clustering Single Cell Rna-Seq Data, Zixia Meng

Theses

In the biological field, the smallest unit of organisms in most biological systems is the single cell, and the classification of cells is an everlasting problem. A central task for analysis of single-cell RNA-seq data is to identify and characterize novel cell types. Currently, there are several classical methods, such as K-means algorithm, spectral clustering, and Gaussian Mixture Models (GMMs), which are widely used to cluster the cells. Furthermore, typical dimensional reduction methods such as PCA, t-SNE, and ZIDA have been introduced to overcome “the curse of dimensionality”. A more recent method scDeepCluster has demonstrated improved and promising performances in …

Fluorescent Probes And Functionalized Nanoparticles For Bioimaging: Synthesis, Photophysical Properties And Applications, Xinglei Liu

Dissertations

The development of new organic molecular probes with excellent photophysical properties and high fluorescence quantum yields is of considerable interest to many research areas including one- and two-photon fluorescence microscopy, fluorescence-based sensing methodologies, and cancer therapy. Series of organic linear-/non-linear optical molecules including squaraine derivatives, and fluorene derivatives as well as other bioconjugates are designed and synthesized during the doctoral study for the aim of ion detection (Chapter 5), photo dynamic therapy, and deep-tissue imaging (Chapter 4). These optical probes are capable of absorbing light in the near infrared (NIR) window and thus have deeper penetration and cause less photodamage …