Engineering Commons^™

Multiphoton Microscopy And Deep Learning Neural Networks For The Automated Quantification Of In Vivo, Label-Free Optical Biomarkers Of Skin Wound Healing, Jake D. Jones

Graduate Theses and Dissertations

Non-healing ulcerative wounds that occur frequently in diseases such as diabetes are challenging to diagnose and treat due to numerous possible etiologies and the variable efficacy of wound care products. With advanced age, skin wound healing is often delayed, leaving elderly patients at high risk for developing these chronic injuries. As it is challenging to discriminate age-related delays from disease-related chronicity, there is a critical need to develop new quantitative biomarkers that are sensitive to wound status. Multiphoton microscopy (MPM) techniques are well-suited for 3D imaging of epithelia and are capable of non-invasively detecting metabolic cofactors (NADH and FAD) without …

Deep Learning-Based, Passive Fault Tolerant Control Facilitated By A Taxonomy Of Cyber-Attack Effects, Dean C. Wardell

Theses and Dissertations

In the interest of improving the resilience of cyber-physical control systems to better operate in the presence of various cyber-attacks and/or faults, this dissertation presents a novel controller design based on deep-learning networks. This research lays out a controller design that does not rely on fault or cyber-attack detection. Being passive, the controller’s routine operating process is to take in data from the various components of the physical system, holistically assess the state of the physical system using deep-learning networks and decide the subsequent round of commands from the controller. This use of deep-learning methods in passive fault tolerant control …

Material Evaluation And Structural Monitoring Of Early-Age Masonry Structures, Kyle Dunphy

Electronic Thesis and Dissertation Repository

During the initial construction period, “early-age” masonry walls are susceptible to lateral loads induced by wind or earthquake, which may result in damages or catastrophic failures. To mitigate such consequences at construction sites, temporary bracings are adopted to provide lateral support to masonry walls until they are matured enough to serve as the inherent lateral system of the structure. However, current temporary bracing guidelines provide oversimplified design due to the lack of available information on the material properties of early-age masonry. Moreover, there are no existing techniques for monitoring masonry walls to detect cracks due to construction activities. …

Graphical Convolution Network Based Semi-Supervised Methods For Detecting Pmu Data Manipulation Attacks, Wenyu Wang

Theses and Dissertations

With the integration of information and communications technologies (ICTs) into the power grid, electricity infrastructures are gradually transformed towards smart grid and power systems become more open to and accessible from outside networks. With ubiquitous sensors, computers and communication networks, modern power systems have become complicated cyber-physical systems. The cyber security issues and the impact of potential attacks on the smart grid have become an important issue. Among these attacks, false data injection attack (FDIA) becomes a growing concern because of its varied types and impacts. Several detection algorithms have been developed in the last few years, which were model-based, …

Deep Learning For Quantitative Motion Tracking Based On Optical Coherence Tomography, Peter Abdelmalak

Theses

Optical coherence tomography (OCT) is a cross-sectional imaging modality based on low coherence light interferometry. OCT has been widely used in diagnostic ophthalmology and has found applications in other biomedical fields such as cancer detection and surgical guidance.

In the Laboratory of Biophotonics Imaging and Sensing at New Jersey Institute of Technology, we developed a unique needle OCT imager based on a single fiber probe for breast cancer imaging. The needle OCT imager with sub-millimeter diameter can be inserted into tissue for minimally invasive in situ breast imaging. OCT imaging provides spatial resolution similar to histology and has the potential …

Development Of Fully Balanced Ssfp And Computer Vision Applications For Mri-Assisted Radiosurgery (Mars), Jeremiah Sanders

Dissertations & Theses (Open Access)

Prostate cancer is the second most common cancer in men and the second-leading cause of cancer death in men. Brachytherapy is a highly effective treatment option for prostate cancer, and is the most cost-effective initial treatment among all other therapeutic options for low to intermediate risk patients of prostate cancer. In low-dose-rate (LDR) brachytherapy, verifying the location of the radioactive seeds within the prostate and in relation to critical normal structures after seed implantation is essential to ensuring positive treatment outcomes.

One current gap in knowledge is how to simultaneously image the prostate, surrounding anatomy, and radioactive seeds within the …

Medical Image Segmentation With Deep Learning, Chuanbo Wang

Theses and Dissertations

Medical imaging is the technique and process of creating visual representations of the body of a patient for clinical analysis and medical intervention. Healthcare professionals rely heavily on medical images and image documentation for proper diagnosis and treatment. However, manual interpretation and analysis of medical images is time-consuming, and inaccurate when the interpreter is not well-trained. Fully automatic segmentation of the region of interest from medical images have been researched for years to enhance the efficiency and accuracy of understanding such images. With the advance of deep learning, various neural network models have gained great success in semantic segmentation and …

Multiscale Modeling And Simulation Of Clutter In Isar Imaging, Jon Mitchell

Electrical Engineering Dissertations

Clutter is common in applications of radar imaging and can adversely impact target imaging by contributing scattered energy that is not accounted for in target signal models. One potential source of clutter is moving foliage in the vicinity of the target, such as a target embedded in a forest. ISAR imaging of moving clutter results in an equivalent current image that changes over each imaging sample. The stochastic nature of this clutter equivalent current presents challenges in detecting and imaging a weak embedded target using traditional algorithms. This dissertation proposes a multiscale model and analysis method to characterize the multiscale …

Vehicle Velocity Prediction Using Artificial Neural Networks And Effect Of Real-World Signals On Prediction Window, Tushar Dnyaneshwar Gaikwad

Masters Theses

Prediction of vehicle velocity is essential since it can realize improvements in the fuel economy/energy efficiency, drivability, and safety. Many publications address velocity prediction problems, yet there is a need for the understanding effect of different signals for the prediction. There are numerous new sensor and signal technologies like vehicle-to-vehicle and vehicle-to-infrastructure communication that can be used to obtain comprehensive datasets. Several references considered deterministic and stochastic approaches that use the datasets as input to determine future operation predictions. These approaches include different traffic models and artificial neural networks such as Markov chain, nonlinear autoregressive model, Gaussian function, and recurrent …

Object Detection With Deep Learning To Accelerate Pose Estimation For Automated Aerial Refueling, Andrew T. Lee

Theses and Dissertations

Remotely piloted aircraft (RPAs) cannot currently refuel during flight because the latency between the pilot and the aircraft is too great to safely perform aerial refueling maneuvers. However, an AAR system removes this limitation by allowing the tanker to directly control the RP A. The tanker quickly finding the relative position and orientation (pose) of the approaching aircraft is the first step to create an AAR system. Previous work at AFIT demonstrates that stereo camera systems provide robust pose estimation capability. This thesis first extends that work by examining the effects of the cameras' resolution on the quality of pose …

Analysis Of Feature Extraction In Knee Cartilage Semantic Segmentation Convolutional Neural Networks, Logan Thorneloe

Undergraduate Honors Theses

Recent advances in deep learning and convolutional neural networks (CNNs) have shown promise for automatic segmentation in magnetic resonance images. However, because of the stochastic nature of the training process, it is difficult to interpret what information networks learn to represent. This study explores multiple difference metrics between networks to determine semantic relationships between knee cartilage tissues. It explores how differences in learned weights and output activations between networks can be used to express these relationships. These findings are further supported by training multi-class networks to segment multiple tissues to compare network accuracy across different tissue combinations. This study shows …

Improved Ground-Based Monocular Visual Odometry Estimation Using Inertially-Aided Convolutional Neural Networks, Josiah D. Watson

Theses and Dissertations

While Convolutional Neural Networks (CNNs) can estimate frame-to-frame (F2F) motion even with monocular images, additional inputs can improve Visual Odometry (VO) predictions. In this thesis, a FlowNetS-based [1] CNN architecture estimates VO using sequential images from the KITTI Odometry dataset [2]. For each of three output types (full six degrees of freedom (6-DoF), Cartesian translation, and transitional scale), a baseline network with only image pair input is compared with a nearly identical architecture that is also given an additional rotation estimate such as from an Inertial Navigation System (INS). The inertially-aided networks show an order of magnitude improvement over the …

Improving Aeromagnetic Calibration Using Artificial Neural Networks, Mitchell C. Hezel

Theses and Dissertations

The Global Positioning System (GPS) has proven itself to be the single most accurate positioning system available, and no navigation suite is found without a GPS receiver. Even basic GPS receivers found in most smartphones can easily provide high quality positioning information at any time. Even with its superb performance, GPS is prone to jamming and spoofing, and many platforms requiring accurate positioning information are in dire need of other navigation solutions to compensate in the event of an outage, be the cause hostile or natural. Indeed, there has been a large push to achieve an alternative navigation capability which …

Smart Distributed Generation System Event Classification Using Recurrent Neural Network-Based Long Short-Term Memory, Shuva Das

Electronic Theses and Dissertations

High penetration of distributed generation (DG) sources into a decentralized power system causes several disturbances, making the monitoring and operation control of the system complicated. Moreover, because of being passive, modern DG systems are unable to detect and inform about these disturbances related to power quality in an intelligent approach. This paper proposed an intelligent and novel technique, capable of making real-time decisions on the occurrence of different DG events such as islanding, capacitor switching, unsymmetrical faults, load switching, and loss of parallel feeder and distinguishing these events from the normal mode of operation. This event classification technique was designed …

Video And Image Super-Resolution Via Deep Learning With Attention Mechanism, Xuan Xu

Graduate Theses, Dissertations, and Problem Reports

Image demosaicing, image super-resolution and video super-resolution are three important tasks in color imaging pipeline. Demosaicing deals with the recovery of missing color information and generation of full-resolution color images from so-called Color filter Array (CFA) such as Bayer pattern. Image super-resolution aims at increasing the spatial resolution and enhance important structures (e.g., edges and textures) in super-resolved images. Both spatial and temporal dependency are important to the task of video super-resolution, which has received increasingly more attention in recent years. Traditional solutions to these three low-level vision tasks lack generalization capability especially for real-world data. Recently, deep learning methods …