MS, University of Cincinnati, 2014, Engineering and Applied Science: Electrical Engineering

Biometric technology is playing a vital role in the present day due to the rapid development of secure systems and home automation that have made our lives easier. But the question arises as to how far these systems are secure. With advances in hacking, the traditional username and password security protocol is not optimal for all security based systems. Though fingerprint identification systems provided a path-breaking solution, there are many methods to forge fingerprints. While other technologies like voice recognition, iris recognition, etc., co-exist, the security and safety of these technologies are also open to question. The major objective of this thesis is to provide enhanced security through a biometrics based embedded system using the technique of Vascular Pattern Recognition or Vein Pattern Recognition (VPR). Another objective is to enhance the vascular pattern image through various image processing techniques. Another target is to reduce the Comparison for Result (CFR) time by a significant factor. Finally, the aim is to implement this VPR based embedded system in a real time software environment. For the system we implemented, our experiments achieved a false accept rate of 0% and a false reject rate of 6.34%. Furthermore, it has been demonstrated in our research that the Speeded Up Robust Features (SURF) algorithm is faster than its predecessor algorithm Scale Invariant Feature Transform (SIFT). The principal conclusion of the thesis is that a safe and secure system can be developed on a small scale with precise results. Given the resources, this system could be extended to a larger scale and customized for a wide range of applications.

Committee: Carla Purdy Ph.D. (Committee Chair); Wen Ben Jone Ph.D. (Committee Member); George Purdy Ph.D. (Committee Member) Subjects: Computer Engineering

Master of Science, The Ohio State University, 2005, Graduate School

Committee: Not Provided (Other) Subjects:

Master of Science, The Ohio State University, 1969, Graduate School

Committee: Not Provided (Other) Subjects:

Master of Science, The Ohio State University, 2005, Graduate School

Committee: Not Provided (Other) Subjects:

MS, University of Cincinnati, 2024, Engineering and Applied Science: Mechanical Engineering

The COVID-19 pandemic highlighted the need for improved and precise prediction of the spatiotemporal trends of epidemic transmission. An optimized epidemic model is crucial for effectively forecasting flow of infection. By optimizing the model parameters, they can provide valuable insights into the dynamics of infection transmission and this degree of tuning helps health officials and policymakers to make data-driven decisions regarding disease control strategies, allocation of resources, and planning for healthcare. Therefore, it highlights the need of implementing reliable optimizing strategies in case of epidemic models. Similarly, the basic and effective reproductive numbers (R0, Re) are quantitative metrics widely used for estimating the rate at which the infection propagates. The limitations of existing techniques for estimating R0 and Re points the need for novel approaches to accurately estimate them using the available data. This initial part of this study presents the development of a custom GA which is capable of efficiently searching for the parameters of an epidemic model in any specified geographical region and time period. Following this, a novel computational framework for predicting the reproduction numbers from true infection data has been presented. The computational framework is derived from a reaction-diffusion based PDE epidemic model which involves fundamental mathematical derivations for obtaining their values. The PDE model is optimized using the proposed GA and the model output using the optimized parameters is found to be in correspondence with the ground truth COVID-19 data of Hamilton county, Ohio. Subsequently, the established framework for calculating the reproduction numbers was applied on the optimized model and their predictions are found to correlate with the true incidence data. In addition, these predictions are compared with a commonly used retrospective method (Wallinga-Teunis) and are found to be in harmony thereby est (open full item for complete abstract)

Committee: Manish Kumar Ph.D. (Committee Chair); Subramanian Ramakrishnan Ph.D. (Committee Member); Shelley Ehrlich M.D. (Committee Member); Derek Wolf Ph.D. (Committee Member) Subjects: Mechanical Engineering

PhD, University of Cincinnati, 2023, Engineering and Applied Science: Computer Science and Engineering

Deep learning techniques are being unified for decision support in various applications. However, it remains challenging to train robust deep learning models, due to the inherent insufficient labeled data that is usually time-consuming and labor-intensive. Self-supervised learning is a feature representation learning paradigm to learn robust features from insufficient annotated datasets. It contains two types of task stages, including the pretext task and the downstream task. The model is typically pre-trained with the pretext task in an unsupervised manner, where the data itself provides supervision. Afterward, the model is fine-tuned in a real downstream supervised task. Although self-supervised learning can effectively learn the robust latent feature representations and reduce human annotation efforts, it highly relies on designing efficient pretext tasks. Therefore, studying effective pretext tasks is desirable to learn more effective features and further improve the model prediction performance for decision support. In self-supervised learning, pretext tasks with deep metric/contrastive learning styles received more and more attention, as the learned distance representations are useful to capture the similarity relationship among samples and further improve the performance of various supervised or unsupervised learning tasks. In this dissertation proposal, we survey the recent state-of-the-art self-supervised learning methods and propose several new deep metric and contrastive learning strategies for learning effective features. Firstly, we propose a new deep metric learning method for image recognition. The proposed method learns an effective distance metric from both geometric and probabilistic space. Secondly, we develop a novel contrastive learning method using the Bregman divergence, extending the contrastive learning loss function into a more generalized divergence form, which improves the quality of self-supervised learned feature representation. (open full item for complete abstract)

Committee: Anca Ralescu Ph.D. (Committee Chair); Kenneth Berman Ph.D. (Committee Member); Dan Ralescu Ph.D. (Committee Member); Lili He Ph.D. (Committee Member); Yizong Cheng Ph.D. (Committee Member) Subjects: Computer Science

Master of Science (MS), Bowling Green State University, 2023, Forensic Science

Bloodstain pattern analysis (BPA) on absorbent surfaces such as fabrics is far more complex compared to its application on hard, nonporous surfaces. This has led to a push for research into understanding the applicability of BPA on porous substrates. Angle of impact, directionality, and pattern type are commonly interpreted from bloodstains, but may differ between fabrics. Blood drops were deposited on six different fabrics (polyester, poly/span, cotton, modal, rayon, and nylon) and a glass substrate at two impact angles: 30 degrees and 10 degrees. An angle of impact was calculated for each bloodstain. Calculated angles of impact for cotton, polyester, and glass were statistically similar to the known theoretical; poly/span, modal, and nylon significantly underestimated the theoretical angle of impact. Impact and other bloodstain patterns were created on the six fabrics, compiled into a survey questionnaire, and sent to trained bloodstain pattern analysts. Respondents answered the survey questions differently based on their amount of training and experience, pattern type, and substrate type. The least experienced group provided the bulk of pattern misclassifications, whereas the most experienced groups did not classify the majority of patterns. By analyzing both how blood interacts with fabrics and the ability of trained practitioners to interpret bloodstains deposited on fabric, guidelines can eventually be developed for when it is appropriate to perform bloodstain pattern analysis on porous substrates such as fabric.

Committee: Travis Worst Ph.D. (Committee Chair); Jeremy Canfield M.S. (Committee Member); Crystal Oechsle Ph.D. (Committee Member) Subjects: Biology; Physics

Doctor of Philosophy, The Ohio State University, 2023, Comparative Biomedical Sciences

Urinary tract infections (UTIs), including cystitis and pyelonephritis, are one of the most common infections across species. Approximately 80% of these infections are caused by uropathogenic Escherichia coli (UPEC). The host's innate immune response is paramount in the defense against UPEC and is initiated by urinary tract epithelial cells with subsequent recruitment of leukocytes. Despite a robust immune response, UPEC can persist in the urinary tract, can cause recurrent infections, and can be resistant to antibiotic therapy. There is a growing and imminent risk for antibiotic resistant UTIs, thus necessitating investigations into alternative therapeutic options. One possibility is utilizing mechanisms to enhance existing host innate immune defenses; however, our understanding of innate immunity against UPEC, especially in the kidney, is limited. In this work, we aimed to characterize the host immune response to UPEC in cell culture and in various important mouse models of UTI to identify potential targets for augmenting host defense against UPEC. The first portion of this thesis investigates the in vitro pathogenesis of UPEC in one of the host's most important UTI renal defenders, the kidney collecting duct intercalated cell. Previous work from our lab and others have shown that intercalated cells are required for protection against UPEC due to direct binding to these cells, their role in immune cell recruitment, and secretion of bactericidal antimicrobial peptides. We found that upon UPEC infection, intercalated cells activate multiple innate immune pathways including those associated with pattern recognition receptor (PRR) signaling. We identified that nucleotide oligomerization domain (NOD) 2 and Toll-like receptor (TLR) 4 are two intercalated cell PRRs that, upon initial activation, can protect against subsequent UPEC infection. Additionally, NOD2 activation induces upregulation of multiple innate immune pathway genes along with genes associated with vari (open full item for complete abstract)

Committee: John David Spencer (Advisor); Kara Corps (Committee Member); Rachel Cianciolo (Committee Member); Brian Becknell (Committee Member); Sheryl Justice (Advisor) Subjects: Biology; Immunology; Microbiology; Molecular Biology; Pathology

Doctor of Philosophy, Case Western Reserve University, 2022, Biomedical Engineering

Non-small cell lung cancer tumors follow an orderly progression from adenocarcinoma in situ (AIS) to minimally invasive carcinoma (MIA) and invasive adenocarcinoma (INV). Currently, there is no definite biomarker to access the level of invasion and detect invasive disease in these early lepidic lesions using radiographic scans, which would ideally help in surgery planning for these patients. Within the early-stage NSCLC cohort, while all the patients will receive the surgery, a significant portion of patients (up to 50\%) will develop recurrence. Although most of these patients are eligible to receive adjuvant chemotherapy (chemo), not all patients will receive the added benefits. In the more advanced NSCLC setting, immunotherapy (IO) has shown promising survival improvement, but only a fraction (20\%) of patients will respond to IO, and a fraction of patients (8\%) would, in fact, receive adverse effects of it, and cancer would spread rapidly (hyperprogression). Most of the current AI methods developed in this field are based on a single modality. However, information across different modalities and scales may hold complementary information, and integrating them together may enhance the performance of AI models. In addition, most of the developed AI models lack interpretability, an essential element for successfully transitioning these AI methods into clinical practices. In this dissertation, we introduced new interpretable AI biomarkers that use textural patterns on radiographic scans, known as Radiomics, and combine these biomarkers across multiple modalities and scales for NSCLC and COVID-19 patients. The Radiomic features were analyzed from inside the tumor region as well as from the area immediately surrounding the nodule. Furthermore, we integrated the clinical features into Radiomics Model by using novel techniques. We also created a human-machine integrated model using Radiologists' scores combined with Radiomic Analysis. Lastly, we used pathology data (open full item for complete abstract)

Committee: Anant Madabhushi (Advisor) Subjects: Biomedical Engineering; Biomedical Research

Doctor of Philosophy, The Ohio State University, 2019, Food Science and Technology

Food adulteration and counterfeiting is a major worldwide problem with a cost of as much as $15 billion annually and affecting nearly 10% of all food products on the market. Besides its economic impact, public health risks could cause far more consequences to the related food industry or food company. Food fraud has been conducted since ancient times, and it is still a worldwide public concern, and a leading cause of trade problems internationally, olive oil and wine were the first counterfeit foods followed by fruit juices, spices, tea, milk, honey, and saffron. Advances in vibrational spectroscopy instruments have made possible rapid material screening with minimal sample preparation and training. The overall objective of this study was to establish a reliable ingredient label verification program(s) for edible oils and fruit juices using portable mid-infrared and Raman spectroscopic techniques combined with pattern recognition analysis. Our first aim evaluated an untargeted approach for authentication of edible oils used in the manufacturing of potato chips by combining the fingerprinting capabilities of a portable 5-reflection attenuated total reflectance infrared (FT-ATR) spectrometer combined with supervised pattern recognition. Oils were characterized by reference methods. Based on the fatty acid composition, we identified ten different frying oils that were used by manufacturers in producing the potato chips. Our data strongly supports that the IR technology can be used by snack food industry and governmental agencies to monitor authentication of frying oils and present great potential for efficient in-situ surveillance of food ingredients. The second study aimed to develop a non-targeted approach to authenticate EVOOs using vibrational spectroscopy (FT-IR and Raman) in combination with pattern recognition analysis. The samples were classified in 4 different groups as EVOO, virgin olive oil (VOO), lower quality olive oils, and olive oils adulterated with (open full item for complete abstract)

Committee: Luis Rodriguez-Saona (Advisor); Monica Giusti (Committee Member); John Litchfield (Committee Member); Lynn Knipe (Committee Member) Subjects: Food Science

PhD, University of Cincinnati, 2018, Engineering and Applied Science: Mechanical Engineering

System health assessment, as one of the most critical tasks in industrial data analytics, focuses on determining the current health condition and detecting the incipient fault. Recently, it has been challenging that the conventional strategy, which relies on a static health reference model along with a fixed threshold, is asked to fulfill the assessment requirements in the nonstationary monitoring environment. The dynamic data contexts might bring incorrect health estimation to the system. This dissertation presents an enhanced systematic online health assessment approach with adaptive self-learning techniques. The method enables the identification of novel working condition states, such as new rotating speed or processing recipe, and the recognition of new degradation extent in the arriving monitoring data, and includes them into the prior learning models. Hence, such continuously growing model could achieve the assessment more efficiently and accurately. This research work proposes the methodology of the enhanced health assessment approach, along with detailed technologies utilized in each implementation step, including a self-learning technique, a change detection and recognition strategy, and a clustering algorithm. Through a toy case on a rotor test bed, the dissertation intuitively described the detailed assessment process and demonstrated that the proposed approach, compared with the static model solution, could successfully capture the newly encountered patterns in the testing data. The feasibility of the proposed approach was demonstrated by two industrial use cases. For the semiconductor manufacturing process monitoring case, the proposed approach was able to correctly estimate the health states of the data measured from different experiments while being trained by one experiment observations. Additionally, it surpassed two existed assessment methods with higher overall assessment accuracy. For the power electronics modules monitoring case, the (open full item for complete abstract)

Committee: Jay Lee Ph.D. (Committee Chair); Thomas Richard Huston Ph.D. (Committee Member); Jay Kim Ph.D. (Committee Member); Manish Kumar Ph.D. (Committee Member) Subjects: Mechanical Engineering

Master of Science in Electrical Engineering (MSEE), Wright State University, 2017, Electrical Engineering

Point set classification methods are used to identify targets described by a spatial collection of points, each represented by a set of attributes. Relative to traditional classification methods based on fixed and ordered feature vectors, point set methods require additional robustness to obscured and missing features, thus necessitating a complex correspondence process between testing and training data. The correspondence problem is efficiently solved via spatial pyramid histograms and associated matching algorithms, however the storage requirements and classification complexity grow linearly with the number of training data points. In this thesis, we develop optimal methods of identifying salient point-features that are most discriminative in a given classification problem. We build upon a logistic regression framework and incorporate a sparsifying prior to both prune non-salient features and prevent overfitting. We present results on synthetic data and measured data from a fingerprint database where point-features are identified with minutia locations. We demonstrate that by identifying salient minutia, the training database may be reduced by 94\% without sacrificing fingerprint identification performance. additionally, we demonstrate that the regularization provided by saliency determination provides improved robustness over traditional pyramid histogram methods in the presence of point migration in noisy data.

Committee: Joshua Ash Ph.D. (Advisor); Arnab Shaw Ph.D. (Committee Member); Steve Gorman Ph.D. (Committee Member) Subjects: Electrical Engineering

PhD, University of Cincinnati, 2017, Engineering and Applied Science: Mechanical Engineering

Maintaining health model robustness has always been a challenge in prognostics and health management. Research on developing advanced machine learning algorithms has shown great promise, but the prognostic performance is limited when the feature quality is poor. This thesis proposes an extensible preprocessing methodology that applies time series pattern recognition to transient-rich and background-rich systems for robust prognostics and health monitoring. This method recognizes patterns-of-interest accurately to facilitate exact extraction of diagnostic information, namely, features. It takes three phases to realize exact feature extraction. First, hierarchical time series classifiers filter out the signals with few critical patterns and prepare the pattern recognition tools for segmentation. Second, time series pattern recognition identifies and segments the patterns-of-interest. Third, extract pattern-specific features as the input for health modeling. The developed exact feature extraction method is validated on two case studies: semiconductor etching process health monitoring and gas type classification using uncalibrated chemical sensors in complex environment. The proposed method is validated to outperform conventional feature extraction such as summary statistics and observation in both studies. The benefits of exact feature extraction include accuracy, consistency, generality, and extensibility. The recognition of patterns enables accurate description of critical process properties and accelerates segmentation compared to human observation. The extracted features are more consistent in healthy condition and more sensitive to faults. Also, the pattern recognition tools are designed for general engineering systems which can be applied to a wide range of industries. Besides, the semi-automated process allows human intervention to include additional patterns for an extensible and customized solution. This thesis embraces domain knowledge and attempts to ge (open full item for complete abstract)

Committee: Jay Lee Ph.D. (Committee Chair); Jay Kim Ph.D. (Committee Member); James Moyne Ph.D. (Committee Member); Jing Shi Ph.D. (Committee Member) Subjects: Mechanical Engineering; Mechanics

Doctor of Philosophy (Ph.D.), University of Dayton, 2017, Electrical and Computer Engineering

The texture of objects in digital images is an important property that has been utilized in many computer vision and image analysis applications, such as pattern recognition, object classification, and region segmentation. Despite its frequent usage and many attempts to describe it in general terms, the texture lacks a precise definition. This makes the development of new texture descriptors a big challenge. In addition, researchers interest has recently spread into the dynamic texture (video domain), where the problem becomes more challenging. The main goal of feature description and representation techniques is to extract features from the image that are distinct and stable under different conditions during the image acquisition process. Texture descriptors can be generally classified into structural and statistical approaches. The structural methods consider the texture as a repetition of some primitives, with a specific rule of placement, while the statistical techniques characterize the stochastic properties of the spatial distribution of gray levels in an image using the gray tone co-occurrence matrix. In this work, we propose a combination of the structural and statistical approaches that can be utilized to recognize a variety of different textures, named High Order Local Directional Pattern (HOLDP) for still image based feature extraction (static texture) as well as High Order Volumetric Directional Pattern (HOVDP) for video based feature extraction (dynamic texture). Recently, the conventional Local Directional Pattern (LDP) has received a great deal of attention in face recognition applications. However, it only describes the micro structures of the texture images because it considers only a small neighborhood size. In fact, our proposed HOLDP descriptor can capture more detailed discriminative information by not only extracting the micro structures but also the macro structures of the texture images, which can be done by the help of a pyramidal mu (open full item for complete abstract)

Committee: Vijayan Asari (Advisor); Russell Hardie (Committee Member); Eric Balster (Committee Member); Youssef Raffoul (Committee Member) Subjects: Electrical Engineering; Engineering

Doctor of Philosophy, The Ohio State University, 1987, Graduate School

Committee: Not Provided (Other) Subjects: Engineering

Doctor of Philosophy, The Ohio State University, 1986, Graduate School

Committee: Not Provided (Other) Subjects: Physics

Doctor of Philosophy, The Ohio State University, 1985, Graduate School

Committee: Not Provided (Other) Subjects: Engineering

Doctor of Philosophy, The Ohio State University, 1976, Graduate School

Committee: Not Provided (Other) Subjects: Engineering

Doctor of Philosophy, The Ohio State University, 1976, Graduate School

Committee: Not Provided (Other) Subjects: Computer Science

Doctor of Philosophy, The Ohio State University, 1975, Graduate School

Committee: Not Provided (Other) Subjects: Computer Science