Engineering Commons^™

A Design Strategy To Improve Machine Learning Resiliency Of Physically Unclonable Functions Using Modulus Process, Yuqiu Jiang

Theses and Dissertations

Physically unclonable functions (PUFs) are hardware security primitives that utilize non-reproducible manufacturing variations to provide device-specific challenge-response pairs (CRPs). Such primitives are desirable for applications such as communication and intellectual property protection. PUFs have been gaining considerable interest from both the academic and industrial communities because of their simplicity and stability. However, many recent studies have exposed PUFs to machine-learning (ML) modeling attacks. To improve the resilience of a system to general ML attacks instead of a specific ML technique, a common solution is to improve the complexity of the system. Structures, such as XOR-PUFs, can significantly increase the nonlinearity …

Image-Based Cancer Diagnosis Using Novel Deep Neural Networks, Hosein Barzekar

Theses and Dissertations

Cancer is the major cause of death in many nations. This serious illness can only be effectivelytreated if it is diagnosed early. In contrast, biomedical imaging presents challenges to both clinical institutions and researchers. Physiological anomalies are often characterized by modest modifications in individual cells or tissues, making them difficult to detect visually. Physiological anomalies are often characterized by slight abnormalities in individual cells or tissues, making them difficult to detect visually. Traditionally, anomalies are diagnosed by radiologists and pathologists with extensive training. This procedure, however, demands the participation of professionals and incurs a substantial expense, making the classification of …

Advanced Analytics In Smart Manufacturing: Anomaly Detection Using Machine Learning Algorithms And Parallel Machine Scheduling Using A Genetic Algorithm, Meiling He

Theses and Dissertations

Industry 4.0 offers great opportunities to utilize advanced data processing tools by generating Big Data from a more connected and efficient data collection system. Making good use of data processing technologies, such as machine learning and optimization algorithms, will significantly contribute to better quality control, automation, and job scheduling in Smart Manufacturing. This research aims to develop a new machine learning algorithm for solving highly imbalanced data processing problems, implement both supervised and unsupervised machine learning auto-selection frameworks for detecting anomalies in smart manufacturing, and develop a genetic algorithm for optimizing job schedules on unrelated parallel machines. This research also …

A Deep Recurrent Neural Network With Iterative Optimization For Inverse Image Processing Applications, Masaki Ikuta

Theses and Dissertations

Many algorithms and methods have been proposed for inverse image processing applications, such as super-resolution, image de-noising, and image reconstruction, particularly with the recent surge of interest in machine learning and deep learning methods.

As for Computed Tomography (CT) image reconstruction, the most recently proposed methods are limited to image domain processing, where deep learning is used to learn the mapping between a true image data set and a noisy image data set in the image domain. While deep learning-based methods can produce higher quality images than conventional model-based algorithms, these methods have a limitation. Deep learning-based methods used in …

Medical Image Segmentation Using Machine Learning, Masoud Khani

Theses and Dissertations

Image segmentation is the most crucial step in image processing and analysis. It can divide an image into meaningfully descriptive components or pathological structures. The result of the image division helps analyze images and classify objects. Therefore, getting the most accurate segmented image is essential, especially in medical images. Segmentation methods can be divided into three categories: manual, semiautomatic, and automatic. Manual is the most general and straightforward approach. Manual segmentation is not only time-consuming but also is imprecise. However, automatic image segmentation techniques, such as thresholding and edge detection, are not accurate in the presence of artifacts like noise …

An Application Of Clustering And Cluster Update Methods To Boiler Sensor Prediction And Case-Based-Reasoning To Boiler Repair, Timothy Edward Rooney

Theses and Dissertations

Driven by demand from both consumers and manufacturers alike, Internet of Things (IoT)

capabilities are being built into more products. Consumers want more control and access to their

devices, while manufacturers can find data gathered from IoT-capable products invaluable. In

this thesis, we use data from a growing fleet of IoT-connected boilers in the residential, lightcommercial, and medium-commercial ranges to demonstrate a framework for cluster initialization

and updating. We compare two methods of dynamically updating clusters: a sequential method

inspired by sequential K-means clustering and a cohesion-based method called DYNC. A predictive

artificial neural network system demonstrates the effectiveness of …

Internal Fault Diagnosis Of Mmc-Hvdc Based On Classification Algorithms In Machine Learning, Tianyi Jin

Theses and Dissertations

With the development of the HVDC system, MMC-HVDC is now the most advanced technology that has been put into use. In power systems, faults happen during the operation due to natural reasons or devices physical issues, which would cause serious economic losses and other implications. Thus, fault detection and analysis are extremely important, especially in the HVDC system. Existing works in literature mainly focus on the faults detection and analysis on the system side such as short circuit of the AC side, and open circuit of the DC side. However, little attention has been paid to the fault detection and …

Internal Fault Diagnosis Of Mmc-Hvdc Based On Classification Algorithms In Machine Learning, Tianyi Jin

Theses and Dissertations

With the development of the HVDC system, MMC-HVDC is now the most advanced technology that has been put into use. In power systems, faults happen during the operation due to natural reasons or devices physical issues, which would cause serious economic losses and other implications. Thus, fault detection and analysis are extremely important, especially in the HVDC system. Existing works in literature mainly focus on the faults detection and analysis on the system side such as short circuit of the AC side, and open circuit of the DC side. However, little attention has been paid to the fault detection and …

Threshold Free Detection Of Elliptical Landmarks Using Machine Learning, Lifan Zhang

Theses and Dissertations

Elliptical shape detection is widely used in practical applications. Nearly all classical ellipse detection algorithms require some form of threshold, which can be a major cause of detection failure, especially in the challenging case of Moire Phase Tracking (MPT) target images. To meet the challenge, a threshold free detection algorithm for elliptical landmarks is proposed in this thesis. The proposed Aligned Gradient and Unaligned Gradient (AGUG) algorithm is a Support Vector Machine (SVM)-based classification algorithm, original features are extracted from the gradient information corresponding to the sampled pixels. with proper selection of features, the proposed algorithm has a high accuracy …

Bayesian Methods And Machine Learning For Processing Text And Image Data, Yingying Gu

Theses and Dissertations

Classification/clustering is an important class of unstructured data processing problems. The classification (supervised, semi-supervised and unsupervised) aims to discover the clusters and group the similar data into categories for information organization and knowledge discovery. My work focuses on using the Bayesian methods and machine learning techniques to classify the free-text and image data, and address how to overcome the limitations of the traditional methods. The Bayesian approach provides a way to allow using more variations(numerical or categorical), and estimate the probabilities instead of explicit rules, which will benefit in the ambiguous cases. The MAP(maximum a posterior) estimation is used to …

Development And Evaluation Of An Interdisciplinary Periodontal Risk Prediction Tool Using A Machine Learning Approach, Neel Anil Shimpi

Theses and Dissertations

Periodontitis (PD) is a major public health concern which profoundly affects oral health and concomitantly, general health of the population worldwide. Evidence-based research continues to support association between PD and systemic diseases such as diabetes and hypertension, among others. Notably PD also represents a modifiable risk factor that may reduce the onset and progression of some systemic diseases, including diabetes. Due to lack of oral screening in medical settings, this population does not get flagged with the risk of developing PD.

This study sought to develop a PD risk assessment model applicable at clinical point-of-care (POC) by comparing performance of …