Department of Ocean and Mechanical Engineering

Florida Atlantic University Departmental Dashboard Indicators. Department program reviews for College of Engineering and Computer Science, Florida Atlantic University.

Florida Atlantic University Departmental Dashboard Indicators. Department program reviews for College of Engineering and Computer Science, Florida Atlantic University.

Florida Atlantic University Departmental Dashboard Indicators. Department program reviews for College of Engineering and Computer Science, Florida Atlantic University.

Florida Atlantic University Departmental Dashboard Indicators. Department program reviews for College of Engineering and Computer Science, Florida Atlantic University.

Florida Atlantic University Departmental Dashboard Indicators. Department program reviews for College of Engineering and Computer Science, Florida Atlantic University.

Florida Atlantic University Departmental Dashboard Indicators. Department program reviews for College of Engineering and Computer Science, Florida Atlantic University.

This thesis presents the work done to deliver a robotic system that provides assistance to operators at nuclear waste cleaning facilities. The work done to deliver such system was focused on robotic control and tactile sensing abilities. Haptic feedback mechanism was also added to the system to convey information for the operator. First chapter of the thesis introduces the goals and objectives of this project as well as a detailed literature review on the subsystems used. Second chapter presents previous work done in the area of soft robotics. Such work proved important as the haptic feedback mechanism utilizes a soft robotic armband. Third chapter introduces phase one of the main project. This chapter justifies the use of the selected robots and introduces the concept of adding tactile abilities to the robotic hand used. Chapter four introduces phase two of the project that focused on improving phase one system via a new tactile sensor.

Electrical impedance of cells is a sensitive indicator of changes in cellular structure and biophysical characteristics. Integration of electrical impedance sensing in microfluidics can be a useful tool for characterization of blood cells for their disease state, such as sickle cell disease and malaria. The first part of this dissertation presents application of a microfluidics-based electrical impedance sensor for the study of sickle cell disease. Dynamic cell sickling-unsickling process of blood cells in response to cyclic hypoxia was measured. Strong correlation was found between the electrical impedance data and patients’ hematological parameters such as levels of sickle hemoglobin and fetal hemoglobin. In addition, application of electrical impedance spectroscopy in narrow microfluidic channel was used for label-free flow cytometry and non-invasive assay of single sickle cells under controlled oxygen level. We demonstrate the capability of this new technique in differentiating normal red blood cells from sickle cells, as well as sickled cells from unsickled cells, using normoxic and hypoxic conditions. The second part of this dissertation reports an application of electrical impedance sensing for the study of placental malaria. Testing conditions were optimized so that electrical impedance can be used for real time monitoring of different cellular and molecular level variations in this in vitro model of placental malaria. Impedance characteristics of cell proliferation, syncytial fusion and long-term response of BeWo cells to adhesion of infected erythrocytes were obtained and related to the immunostaining results and inflammatory cytokines measurements. Comparing to the conventional optical microscope-based methods, electrical impedance sensing technique can provide a label-free, real-time monitoring tool to study erythrocytes and cytoadhesion, and can further be extended to other disease models and cell types.

Automatic target recognition of unexploded ordnances in side scan sonar imagery has been a struggling task, due to the lack of publicly available side-scan sonar data. Real time image detection and classification algorithms have been implemented to combat this task, however, machine learning algorithms require a substantial amount of training data to properly detect specific targets. Transfer learning methods are used to replace the need of large datasets, by using a pre trained network on the side-scan sonar images. In the present study the implementation of a generative adversarial network is used to generate meaningful sonar imagery from a small dataset. The generated images are then added to the existing dataset to train an image detection and classification algorithm. The study looks to demonstrate that generative images can be used to aid in detecting objects of interest in side-scan sonar imagery.

The thesis objective is to develop protocols that provide analysis and interpretation for data from a pulsed laser serial scanning lidar system for underwater imaging. The specific focus is remotely observing marine organisms in the centimeter size range in the poorly understood mesopelagic realm of the ocean. The Unobtrusive Multi-Static Lidar Imager (UMSLI) system captures an expansive volume scan field with differentiating imaging resolution per planar slice, allowing precise assignment of location for organisms in the field of view. The multi-static highly collimated beams are recorded by a photo-multiplier tube receiver as a time lapse waveform of the returned energy flux, each waveform comprehensibly represents an image pixel in spatially and temporally. Complied lidar waveforms produce an array of returns which signify the magnitude of backscatter from varying sized particles across the observed volume. These volume scans are uniquely evaluated and transformed for each time bin through a processing method which extracts particle characteristics and statistics based on adaptive spatial and temporal techniques. The post processing method aims to greatly extend the capabilities of the lidar imaging system to extract particles. Results of the processing method are presented as particle counts and particle size distributions of the water columns during observed vertical migration periods. Methods are compared with other optical devices for validation, and results are interpreted to better understand the organism distribution in the mesopelagic and their behavior, with respect to diel vertical migrations.