In this thesis, a methodology and framework were created to detect the seawalls accurately and efficiently in low coastal areas and was evaluated in the study area of Hallandale Beach City, Broward County, Florida. Aerial images collected from the Florida Department of Transportation (FDOT) were processed using eCognition Developer software for Multi-Resolution Segmentation and Classification of objects. Two classification approaches, pixel-based image analysis, and the object-based image analysis (OBIA) method were applied for image classification. However, Pixel based classification was discarded for having less accuracy in output. Three techniques within object-based classification-machine learning technique, knowledge-based technique and machine learning followed by knowledge-based technique were used to compare the most efficient method of classification. While performing the machine learning technique, three algorithms: Random Forest, support vector machine and decision tree were applied to test the best algorithm. Of all the approaches used, the combination of machine learning and a knowledge-based method was able to map the sea wall effectively. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2021. / FAU Electronic Theses and Dissertations Collection

Nanostructures fabricated by different methods have become increasingly important for various applications at the cellular level. In order to understand how these nanostructures “behave” and for studying their internalization kinetics, several attempts have been made at tagging and investigating their interaction with living cells. In this study, magnetic iron nanowires with an iron oxide layer are coated with (3-Aminopropyl)triethoxysilane (APTES), and subsequently labeled with a fluorogenic pH-dependent dye pHrodo™ Red, covalently bound to the aminosilane surface. Time-lapse live imaging of human colon carcinoma HCT 116 cells interacting with the labeled iron nanowires is performed for 24 hours. As the pHrodo™ Red conjugated nanowires are non-fluorescent outside the cells but fluoresce brightly inside, internalized nanowires are distinguished from non-internalized ones and their behavior inside the cells can be tracked for the respective time length. A machine learning-based computational framework dedicated to automatic analysis of live cell imaging data, Cell Cognition, is adapted and used to classify cells with internalized and non-internalized nanowires and subsequently determine the uptake percentage by cells at different time points. An uptake of 85 % by HCT 116 cells is observed after 24 hours incubation at NW-to-cell ratios of 200. While the approach of using pHrodo™ Red for internalization studies is not novel in the literature, this study reports for the first time the utilization of a machine-learning based time-resolved automatic analysis pipeline for quantification of nanowire uptake by cells. This pipeline has also been used for comparison studies with nickel nanowires coated with APTES and labeled with pHrodo™ Red, and another cell line derived from the cervix carcinoma, HeLa. It has thus the potential to be used for studying the interaction of different types of nanostructures with potentially any live cell types.

<p>Community interactions based on various parameters in defined niches have been studied to understand their influence on bacterial life. Yet there currently are no models that can depict how spatial interactions control the complex combinatorics of different microbial communities. Biodiversity influences the ecosystem properties of bacterial communities, but the relationship between bacterial biodiversity and function remains to be understood entirely. Here, the focus is on developing a simple and effective platform to study neighborhood interactions between different species of lactic acid bacteria by controlling two metrics – distance and composition. Using this simple platform, I explore 1) how spatial and temporal arrangement between different bacteria affect their interaction in a high throughput manner, 2) how biodiversity can be manipulated in terms of its starting population, the number of species, and species identity. </p>

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Autophagy is an essential quality control process in which proteins and organelles are degraded. In this work, we first extended our understanding of autophagic degradation in disease by investigating the use of acidic nanoparticles to restore autophagic flux in a neurotoxic model of Parkinson Disease (PD). Normal autophagic degradation follows two key steps. First, material is engulfed to form a double-membraned autophagosome. Next, autophagosomes fuse with an acidic lysosome to degrade the inner membrane contents. Insufficient lysosomal acidity results in autophagic flux arrest, and in PC-12 cells, we characterized the use of polymeric nanoparticles as a tool to restore lysosomal acidity and rescue autophagic flux in PC-12 cells. Specifically, in an MPP+ model of neurotoxicity, we demonstrated that formulations of poly(lactic-co-glycolic acid) nanoparticles (PLGA) improved lysosomal acidity, autophagic flux, and cell health significantly, but is likely limited in efficacy by polymer degradation rate. To improve upon this, we developed a new acidic nanoparticle formulated with a novel polymer backbone (termed acNPs), engineered to degrade within lysosomes and release tetrafluorosuccinic acid, a highly potent acid (pKa ~1.6). On the benchtop, these engineered nanoparticles demonstrated both colloidal instability and acid release within a weakly acidic environment (pH 6.0) similar to a diseased lysosome but not at a neutral pH of 7.4. In cells, acNPs effectively decreased lysosomal pH within disease lysosomes, thereby restoring autophagic flux and mitochondrial activity in PC-12 cells. Encouragingly, we also were able to show efficacy of acNPs in 2D and 3D models of the human midbrain. acNPs readily trafficked within the lysosomes of cells in 2D midbrain cultures and 3D midbrain organoids. Similar to PC-12 cells, when we challenged these cells in a model of neurotoxicity, we observed restoration of viability in human organoids following acNP treatment. Next, we addressed some current challenges regarding the quantification of autophagy within cells. We repurposed measures of economic income inequality to quantify the spatial dispersion of LC3 signal intensity in a starvation model of autophagy, and then compared these measures to other image-based measurements based on their ability to represent LC3-II levels, a robust protein marker of the autophagosome. Our analysis showed these indices outperformed all other generated measurements, including the current standard of autophagy research, LC3 puncta counting. Additionally, we also explored the linear decomposition properties of the generalized entropy index and found it a facile way to evaluate autophagic flux within 3D imaging datasets of multicellular systems. Specifically, we revealed a differential response to nutrient depravation between neurons and astrocytes. Finally, we translated this paradigm to a high throughput cell assay where we demonstrated EC50 and IC50 curves, produced from datasets acquired through both confocal and automated widefield fluorescence microscopy. Our results agree with standard cell assays.

It has long been desired to simulate a non-preprogrammed controllable visual scene of the real world in a limited area for the purpose of training, in particular for aviation training. There presently exists a point light source projection system designed to accomplish this, however due to physical limitations, the display image, as compared to the corresponding real world scene which is being simulated, is somewhat distorted and limited. This point light source system is analyzed to determine the distortions and deviations from reality and methods are proposed to improve the display by minimizing the distortions and deviations.

This project is about automating foam quality control. Its purpose is to construct an automated quality-control station that will characterise foam in line with a production line that non-technical persons can efficiently operate. Foam passes through the station on a purpose-built conveyor. A robot then moves a microscope over the foam to capture detailed close-up images. The characterisation is done in software by examining the foam’s pores and measuring it against a set of tweakable parameters. There were some problems in focusing the microscope, but testing resolved the focusing to be non-essential. A database is used with the quality control station to store and sort test data efficiently. The overall quality control station is a success and can characterise foam according to the project’s predefined goals.

In today’s technically advanced society the collection and study of digital images has become an important aspect of various off-line applications that range from medical diagnosis to exploring the Martian surface for traces of water. Various industries have recently started moving towards vision based systems to monitor several of their manufacturing processes. Except for some simple on-line applications, these systems are primarily used to analyze the digital images off-line. This thesis is concerned with developing a more powerful on-line digital image analysis technique which links the fields of traditional digital image processing with a recently devised statistically based image analysis method called multivariate image analysis (MIA). The first part of the thesis introduces the area of traditional digital image processing techniques through a brief literature review of three of its five main classes (image enhancement, restoration, analysis, compression, & synthesis) which contain most of the commonly used operations in this area. This introduction is intended as a starting point for readers who have little background in this field, and as a means of providing sufficient details on these techniques so that they can be used in conjunction with other advanced MIA on-line monitoring operations. MIA of multispectral digital images using latent variable statistical methods (Multi-Way PCA / PLS) is the main topic covered by the second part of this thesis. After reviewing the basic theory of feature extraction using MIA for off-line analyses, a new technique is introduced that extends these ideas for image analyses in on-line applications. Instead of directly using the updated images themselves to monitor a time- varying process, this new technique uses the latent variable space of the image to monitor the increase or decline in the number of pixels belonging to various features of interest. The ability to switch between the images and their latent variable space then allows the user to determine the exact spatial locations of any features of interest. This new method is shown to be ideal for monitoring interesting features from time-varying processes equipped with multispectral sensors. It forms a basis for future on-line industrial process monitoring schemes in those industries that are moving towards automatic vision systems using multispectral digital imagery. / Thesis / Master of Engineering (ME)

Darknet markets are online services behind Tor where cybercriminals trade illegal goods and stolen datasets. In recent years, security analysts and law enforcement start to investigate the darknet markets to study the cybercriminal networks and predict future incidents. However, vendors in these markets often create multiple accounts (i.e., Sybils), making it challenging to infer the relationships between cybercriminals and identify coordinated crimes. In this thesis, we present a novel approach to link the multiple accounts of the same darknet vendors through photo analytics. The core idea is that darknet vendors often have to take their own product photos to prove the possession of the illegal goods, which can reveal their distinct photography styles. To fingerprint vendors, we construct a series deep neural networks to model the photography styles. We apply transfer learning to the model training, which allows us to accurately fingerprint vendors with a limited number of photos. We evaluate the system using real-world datasets from 3 large darknet markets (7,641 vendors and 197,682 product photos). A ground-truth evaluation shows that the system achieves an accuracy of 97.5%, outperforming existing stylometry-based methods in both accuracy and coverage. In addition, our system identifies previously unknown Sybil accounts within the same markets (23) and across different markets (715 pairs). Further case studies reveal new insights into the coordinated Sybil activities such as price manipulation, buyer scam, and product stocking and reselling. / Master of Science / Taking advantage of the high anonymity of darknet, cybercriminals have set up underground trading websites such as darknet markets for trading illegal goods. To understand the relationships between cybercriminals and identify coordinated activities, it is necessary to identify the multiple accounts hold by the same vendor. Apart from manual investigation, previous studies have proposed methods for linking multiple accounts through analyzing the writing styles hidden in the users' online posts, which face key challenges in similar tasks on darknet markets. In this thesis, we propose a novel approach to link multiple identities within the same darknet market or across different markets by analyzing the product photos. We develop a system where a series of deep neural networks (DNNs) are used with transfer learning to extract distinct features from a vendor's photos automatically. Using real-world datasets from darknet markets, we evaluate the proposed system which shows clear advantages over the writing style based system. Further analysis of the results reported by the proposed system reveal new insights into coordinated activities such as price manipulation, buyer scam and product stocking and reselling for those vendors who hold multiple accounts.

The ASTM C457 test has long been a standard used to obtain the air void parameters of concrete materials. These air void parameters provide valuable information that has been linked to the performance of concrete under conditions such as freezing and thawing cycles. The standard test procedure involves linearly traversing a cut and polished section of a concrete specimen while a technician observes it under a microscope. Chord lengths of material constituents that the technician observes along the linear traverse are recorded and later used to calculate air void parameters statistically. This procedure is long and tedious, which makes it susceptible to human error due to operator fatigue. This study proposes and implements a new test method for evaluating concrete air void parameters using an image analysis method. A polishing procedure along with a differential interference contrast microscope are used to obtain high contrast images of material constituents, which provide raw data for the image analysis method. Because of the high contrast that can be obtained, cement paste, air voids in the cement paste, and aggregate materials in the concrete can be distinguished from one another based on these images. An image analysis program has been written for this study which linearly traverses these images and records the chord lengths of material constituents in a similar way to the standard ASTM C457 test. The chord length data must be processed further, however, because features in the images can be truncated by the edge of the image. Correction calculations for this problem are implemented in the image analysis algorithm. Two specimens which have been previously tested using the standard ASTM C457 method by the Virginia Transportation Research Council, (VTRC), are used in this study. The air void parameters obtained using the new test are compared directly with the results obtained by VTRC for the two specimens. Statistical comparisons indicate that the results of the new test are indeed significant, showing the potential it has for practical implementation. There are drawbacks to the test including a long polishing procedure, but this process can be automated. The new test appears to have excellent potential for practical application, but it should be emphasized that the test has only been implemented using materials in two concrete specimens. Further study on a variety of other concrete materials would be required for implementation in a standard procedure. / Master of Science