Physical Sciences and Mathematics Commons^™

Enhancing Urban Life: A Policy-Based Autonomic Smart City Management System For Efficient, Sustainable, And Self-Adaptive Urban Environments, Elham Okhovat

Electronic Thesis and Dissertation Repository

This thesis proposes the concept of the Policy-based Autonomic Smart City Management System, an innovative framework designed to comprehensively manage diverse aspects of urban environments, ranging from environmental conditions such as temperature and air quality to the infrastructure which comprises multiple layers of infrastructure, from sensors and devices to advanced IoT platforms and applications. Efficient management requires continuous monitoring of devices and infrastructure, data analysis, and real-time resource assessment to ensure seamless city operations and improve residents' quality of life. Automating data monitoring is essential due to the vast array of hardware and data exchanges, and round-the-clock monitoring is critical. …

Local Model Agnostic Xai Methodologies Applied To Breast Cancer Malignancy Predictions, Heather Hartley

Electronic Thesis and Dissertation Repository

This thesis examines current state-of-the-art Explainable Artificial Intelligence (XAI) methodologies applicable to breast cancer diagnostics, as well as local model-agnostic XAI methodologies more broadly. It is well known that AI is underutilized in healthcare due to the fact that black box AI methods are largely uninterpretable. The potential for AI to positively affect health care outcomes is massive, and AI adoption by medical practitioners and the community at large will translate to more desirable patient outcomes. The development of XAI is crucial to furthering the integration of AI within healthcare, as it will allow medical practitioners and regulatory bodies to …

Framework For Assessing Information System Security Posture Risks, Syed Waqas Hamdani

Electronic Thesis and Dissertation Repository

In today’s data-driven world, Information Systems, particularly the ones operating in regulated industries, require comprehensive security frameworks to protect against loss of confidentiality, integrity, or availability of data, whether due to malice, accident or otherwise. Once such a security framework is in place, an organization must constantly monitor and assess the overall compliance of its systems to detect and rectify any issues found. This thesis presents a technique and a supporting toolkit to first model dependencies between security policies (referred to as controls) and, second, devise models that associate risk with policy violations. Third, devise algorithms that propagate risk when …

An Investigation Into Time Gazed At Traffic Objects By Drivers, Kolby R. Sarson

Electronic Thesis and Dissertation Repository

Several studies have considered driver’s attention for a multitude of distinct purposes, ranging from the analysis of a driver’s gaze and perception, to possible use in Advanced Driving Assistance Systems (ADAS). These works typically rely on simple definitions of what it means to “see,” considering a driver gazing upon an object for a single frame as being seen. In this work, we bolster this definition by introducing the concept of time. We consider a definition of ”seen” which requires an object to be gazed upon for a set length of time, or frames, before it can be considered as seen …

Towards The Development Of A Cost-Effective Image-Sensing-Smart-Parking Systems (Isensmap), Aakriti Sharma

Electronic Thesis and Dissertation Repository

Finding parking in a busy city has been a major daily problem in today’s busy life. Researchers have proposed various parking spot detection systems to overcome the problem of spending a long time searching for a parking spot. These works include a wide variety of sensors to detect the presence of a vehicle in a parking spot. These approaches are expensive to implement and ineffective in extreme weather conditions in an outdoor parking environment. As a result, a cost-effective, dependable, and time-saving parking solution is much more desirable. In this thesis, we proposed and developed an image processing-based real-time parking-spot …

Framework For Kernel Based Bm3d Algorithm, Mena Abdelrahman Massoud

Electronic Thesis and Dissertation Repository

Patch-based approaches such as Block Matching and 3D collaborative Filtering (BM3D) algorithm represent the current state-of-the-art in image denoising. However, BM3D still suffers from degradation in performance in smooth areas as well as loss of image details, specifically in the presence of high noise levels.

Integrating shape adaptive methods with BM3D improves the denoising outcome including the visual quality of the denoised image; and also maintains image details. In this study, we proposed a framework that produces multiple images using various shapes. These images were aggregated at the pixel or patch levels for both stages in BM3D, and when appropriately …

Computational Methods For Predicting Protein-Protein Interactions And Binding Sites, Yiwei Li

Electronic Thesis and Dissertation Repository

Proteins are essential to organisms and participate in virtually every process within cells. Quite often, they keep the cells functioning by interacting with other proteins. This process is called protein-protein interaction (PPI). The bonding amino acid residues during the process of protein-protein interactions are called PPI binding sites. Identifying PPIs and PPI binding sites are fundamental problems in system biology.

Experimental methods for solving these two problems are slow and expensive. Therefore, great efforts are being made towards increasing the performance of computational methods.

We present DELPHI, a deep learning based program for PPI site prediction and SPRINT, an algorithmic …

Discrimination Of Leucine And Isoleucine In De Novo Peptide Sequencing Using Deep Neural Networks, Bingran Shen

Electronic Thesis and Dissertation Repository

De novo peptide sequencing from tandem MS data is a key technology in proteomics for understanding the structure of proteins, especially for first seen sequences. Although this technique has advanced rapidly in recent years and become more effective, one crucial problem remained unsolved. Due to the isomerism of leucine and isoleucine, they are practically indistinguishable in de novo sequencing using traditional tandem MS data. Some experimental attempts have been made to resolve this ambiguity such as EThCD fragmentation process. In this study, we took a data focused approach rather than only looking for characteristic satellite ions produced by the EThCD …

Automated Anomaly Detection And Localization System For A Microservices Based Cloud System, Priyanka Prakash Naikade

Electronic Thesis and Dissertation Repository

Context: With an increasing number of applications running on a microservices-based cloud system (such as AWS, GCP, IBM Cloud), it is challenging for the cloud providers to offer uninterrupted services with guaranteed Quality of Service (QoS) factors. Problem Statement: Existing monitoring frameworks often do not detect critical defects among a large volume of issues generated, thus affecting recovery response times and usage of maintenance human resource. Also, manually tracing the root causes of the issues requires a significant amount of time. Objective: The objective of this work is to: (i) detect performance anomalies, in real-time, through monitoring KPIs (Key Performance …

A Hybrid Approach To Procedural Dungeon Generation, Mathias Paul Babin

Electronic Thesis and Dissertation Repository

This thesis presents a novel approach to the Procedural Content Generation (PCG) of both maze and dungeon environments. The solution we propose in this thesis borrows techniques from both Procedural Content Generation via Machine Learning as well as Constructive PCG methods. The approach we take involves decomposing the problem of level generation into a series of stages which begins with the production of macro-level functional structures and ends with micro-level aesthetic details; specifically, we train a Deep Convolutional Neural Network to produce high-quality mazes, which in turn, are transformed into the rooms of larger dungeon levels using a constructive algorithm. …

Edge-Cloud Iot Data Analytics: Intelligence At The Edge With Deep Learning, Ananda Mohon M. Ghosh

Electronic Thesis and Dissertation Repository

Rapid growth in numbers of connected devices, including sensors, mobile, wearable, and other Internet of Things (IoT) devices, is creating an explosion of data that are moving across the network. To carry out machine learning (ML), IoT data are typically transferred to the cloud or another centralized system for storage and processing; however, this causes latencies and increases network traffic. Edge computing has the potential to remedy those issues by moving computation closer to the network edge and data sources. On the other hand, edge computing is limited in terms of computational power and thus is not well suited for …

Identifying External Cross-References Using Natural Language Processing (Nlp), Elham Rahmani

Electronic Thesis and Dissertation Repository

[Context and motivation] Software engineers build systems that need to be compliant with relevant regulations. These regulations are stated in authoritative documents from which regulatory requirements need to be elicited. Project contract contains cross-references to these regulatory requirements in external documents. [Problem] Exploring and identifying the regulatory requirements in voluminous textual data is enormously time consuming, and hence costly, and error-prone in sizable software projects. [Principal idea and novelty] We use Natural Language Processing (NLP), Pattern Recognition and Web Scrapping techniques for automatically extracting external cross-references from contractual requirements and prepare a map for representing related external cross-references …

Hierarchical Group And Attribute-Based Access Control: Incorporating Hierarchical Groups And Delegation Into Attribute-Based Access Control, Daniel Servos

Electronic Thesis and Dissertation Repository

Attribute-Based Access Control (ABAC) is a promising alternative to traditional models of access control (i.e. Discretionary Access Control (DAC), Mandatory Access Control (MAC) and Role-Based Access control (RBAC)) that has drawn attention in both recent academic literature and industry application. However, formalization of a foundational model of ABAC and large-scale adoption is still in its infancy. The relatively recent popularity of ABAC still leaves a number of problems unexplored. Issues like delegation, administration, auditability, scalability, hierarchical representations, etc. have been largely ignored or left to future work. This thesis seeks to aid in the adoption of ABAC by filling in …

A New Method To Solve Same-Different Problems With Few-Shot Learning, Yuanyuan Han

Electronic Thesis and Dissertation Repository

Visual learning of highly abstract concepts is often simple for humans but very challenging for machines. Same-different (SD) problems are a visual reasoning task with highly abstract concepts. Previous work has shown that SD problems are difficult to solve with standard deep learning algorithms, especially in the few-shot case, despite the ability of such algorithms to learn abstract features. In this thesis, we propose a new method to solve SD problems with few training samples, in which same-different visual concepts can be recognized by examining similarities between Regions of Interest by using a same-different twins network. Our method achieves state-of-the-art …

A New Algorithm For Primer Design, Debanjan Guha Roy

Electronic Thesis and Dissertation Repository

The Polymerase Chain Reaction (PCR) technology is widely used to create DNA copies. It has impacted many diverse fields including genetics, forensics, molecular paleontology, medical applications and environmental microbiology.

The main object in PCR is a primer, a short single strand of DNA, about 18-25 bases long, that serves as the starting point of DNA synthesis. Primers are essential for DNA replication because the enzymes that catalyze this process, DNA polymerases, can only add new nucleotides to an existing strand of DNA. The PCR starts at the 3’-end of the primer and copies the opposite strand.Designing good primers is essential …

Virtual Sensor Middleware: A Middleware For Managing Iot Data For The Fog-Cloud Platform, Fadi Almahamid

Electronic Thesis and Dissertation Repository

Internet of Things is a massively growing field where billions of devices are connected to the Internet using different protocols and produce an enormous amount of data. The produced data is consumed and processed by different applications to make operations more efficient. Application development is challenging, especially when applications access sensor data since IoT devices use different communication protocols.

The existing IoT architectures address some of these challenges. This thesis proposes an IoT Middleware that provides applications with the abstraction required of IoT devices while distributing the processing of sensor data to provide a real-time or near real-time response and …

Vessel Tree Reconstruction With Divergence Prior, Zhongwen Zhang

Electronic Thesis and Dissertation Repository

Accurate structure analysis of high-resolution 3D biomedical images of vessels is a challenging issue and in demand for medical diagnosis nowadays. Previous curvature regularization based methods [10, 31] give promising results. However, their mathematical models are not designed for bifurcations and generate significant artifacts in such areas. To address the issue, we propose a new geometric regularization principle for reconstructing vector fields based on prior knowledge about their divergence. In our work, we focus on vector fields modeling blood flow pattern that should be divergent in arteries and convergent in veins. We show that this previously ignored regularization constraint can …

Putting Fürer's Algorithm Into Practice With The Bpas Library, Linxiao Wang

Electronic Thesis and Dissertation Repository

Fast algorithms for integer and polynomial multiplication play an important role in scientific computing as well as other disciplines. In 1971, Schönhage and Strassen designed an algorithm that improved the multiplication time for two integers of at most n bits to O(log n log log n). In 2007, Martin Fürer presented a new algorithm that runs in O (n log n · 2 ^O(log* n)) , where log*n is the iterated logarithm of n. We explain how we can put Fürer’s ideas into practice for multiplying polynomials over a prime field Z/pZ, which characteristic is a Generalized Fermat prime of …

Computational Modelling Of Human Transcriptional Regulation By An Information Theory-Based Approach, Ruipeng Lu

Electronic Thesis and Dissertation Repository

ChIP-seq experiments can identify the genome-wide binding site motifs of a transcription factor (TF) and determine its sequence specificity. Multiple algorithms were developed to derive TF binding site (TFBS) motifs from ChIP-seq data, including the entropy minimization-based Bipad that can derive both contiguous and bipartite motifs. Prior studies applying these algorithms to ChIP-seq data only analyzed a small number of top peaks with the highest signal strengths, biasing their resultant position weight matrices (PWMs) towards consensus-like, strong binding sites; nor did they derive bipartite motifs, disabling the accurate modelling of binding behavior of dimeric TFs.

This thesis presents a novel …

Self-Assembly Of Tiles: Theoretical Models, The Power Of Signals, And Local Computing, Amirhossein Simjour

Electronic Thesis and Dissertation Repository

DNA-based self-assembly is an autonomous process whereby a disordered system of DNA sequences forms an organized structure or pattern as a consequence of Watson-Crick complementarity of DNA sequences, without external direction.

Here, we propose self-assembly (SA) hypergraph automata as an automata-theoretic model for patterned self-assembly. We investigate the computational power of SA-hypergraph automata and show that for every recognizable picture language, there exists an SA-hypergraph automaton that accepts this language. Conversely, we prove that for any restricted SA-hypergraph automaton, there exists a Wang Tile System, a model for recognizable picture languages, that accepts the same language.

Moreover, we investigate the …

Computer Vision Problems In 3d Plant Phenotyping, Ayan Chaudhury

Electronic Thesis and Dissertation Repository

In recent years, there has been significant progress in Computer Vision based plant phenotyping (quantitative analysis of biological properties of plants) technologies. Traditional methods of plant phenotyping are destructive, manual and error prone. Due to non-invasiveness and non-contact properties as well as increased accuracy, imaging techniques are becoming state-of-the-art in plant phenotyping. Among several parameters of plant phenotyping, growth analysis is very important for biological inference. Automating the growth analysis can result in accelerating the throughput in crop production. This thesis contributes to the automation of plant growth analysis.

First, we present a novel system for automated and non-invasive/non-contact plant …

Quality Assessment Of The Canadian Openstreetmap Road Networks, Hongyu Zhang

Electronic Thesis and Dissertation Repository

Volunteered geographic information (VGI) has been applied in many fields such as participatory planning, humanitarian relief and crisis management because of its cost-effectiveness. However, coverage and accuracy of VGI cannot be guaranteed. OpenStreetMap (OSM) is a popular VGI platform that allows users to create or edit maps using GPS-enabled devices or aerial imageries. The issue of geospatial data quality in OSM has become a trending research topic because of the large size of the dataset and the multiple channels of data access. The objective of this study is to examine the overall reliability of the Canadian OSM data. A systematic …

Patch-Based Denoising Algorithms For Single And Multi-View Images, Monagi H. Alkinani

Electronic Thesis and Dissertation Repository

In general, all single and multi-view digital images are captured using sensors, where they are often contaminated with noise, which is an undesired random signal. Such noise can also be produced during transmission or by lossy image compression. Reducing the noise and enhancing those images is among the fundamental digital image processing tasks. Improving the performance of image denoising methods, would greatly contribute to single or multi-view image processing techniques, e.g. segmentation, computing disparity maps, etc. Patch-based denoising methods have recently emerged as the state-of-the-art denoising approaches for various additive noise levels. This thesis proposes two patch-based denoising methods for …

Color Separation For Background Subtraction, Jiaqi Zhou

Electronic Thesis and Dissertation Repository

Background subtraction is a vital step in many computer vision systems. In background subtraction, one is given two (or more) frames of a video sequence taken with a still camera. Due to the stationarity of the camera, any color change in the scene is mainly due to the presence of moving objects. The goal of background subtraction is to separate the moving objects (also called the foreground) from the stationary background. Many background subtraction approaches have been proposed over the years. They are usually composed of two distinct stages, background modeling and foreground detection.

Most of the standard background subtraction …

Cultural Diversity In Artificial Societies: Case Studies Of The Maya Peoples, Roberto Ulloa

Electronic Thesis and Dissertation Repository

The existence of cultural diversity in a connected world is paradoxical given that all individuals constantly interact and share information, and that individuals are all part of one giant network of connections. In the long term, it seems logical to assume that everybody should hold the same cultural information and, therefore, the same culture. Yet cultural diversity is still manifest around the globe. Cultural diversity as a phenomenon becomes even more puzzling when we take into account how it survives catastrophic events which regularly befall societies, such as invasions, natural disasters, and civil wars. In this thesis, agent-based computer simulations …

Molecular Distance Maps: An Alignment-Free Computational Tool For Analyzing And Visualizing Dna Sequences' Interrelationships, Rallis Karamichalis

Electronic Thesis and Dissertation Repository

In an attempt to identify and classify species based on genetic evidence, we propose a novel combination of methods to quantify and visualize the interrelationships between thousand of species. This is possible by using Chaos Game Representation (CGR) of DNA sequences to compute genomic signatures which we then compare by computing pairwise distances. In the last step, the original DNA sequences are embedded in a high dimensional space using Multi-Dimensional Scaling (MDS) before everything is projected on a Euclidean 3D space.

To start with, we apply this method to a mitochondrial DNA dataset from NCBI containing over 3,000 species. The …

A Data Fusion Approach To Automated Decision Making In Intelligent Vehicles, Besat Zardosht

Electronic Thesis and Dissertation Repository

The goal of an intelligent transportation system is to increase safety, convenience and efficiency in driving. Besides these obvious advantages, the integration of intelligent features and autonomous functionalities on vehicles will lead to major economic benefits from reduced fuel consumption to efficient exploitation of the road network.

While giving this information to the driver can be useful, there is also the possibility of overloading the driver with too much information. Existing vehicles already have some mechanisms to take certain actions if the driver fails to act. Future vehicles will need more complex decision making modules which receive the raw data …

Agora: A Knowledge Marketplace For Machine Learning, Mauro Ribeiro

Electronic Thesis and Dissertation Repository

More and more data are becoming part of people's lives. With the popularization of technologies like sensors, and the Internet of Things, data gathering is becoming possible and accessible for users. With these data in hand, users should be able to extract insights from them, and they want results as soon as possible. Average users have little or no experience in data analytics and machine learning and are not great observers who can collect enough data to build their own machine learning models. With large quantities of similar data being generated around the world and many machine learning models being …

Using Physical And Social Sensors In Real-Time Data Streaming For Natural Hazard Monitoring And Response, Yelena Kropivnitskaya

Electronic Thesis and Dissertation Repository

Technological breakthroughs in computing over the last few decades have resulted in important advances in natural hazards analysis. In particular, integration of a wide variety of information sources, including observations from spatially-referenced physical sensors and new social media sources, enables better estimates of real-time hazard. The main goal of this work is to utilize innovative streaming algorithms for improved real-time seismic hazard analysis by integrating different data sources and processing tools into cloud applications. In streaming algorithms, a sequence of items from physical and social sensors can be processed in as little as one pass with no need to store …

Advanced Driving Assistance Prediction Systems, Maedeh Hesabgar

Electronic Thesis and Dissertation Repository

Future automobiles are going to experience a fundamental evolution by installing semiotic predictor driver assistance equipment. To meet these equipment, Continuous driving-behavioral data have to be observed and processed to construct powerful predictive driving assistants. In this thesis, we focus on raw driving-behavioral data and present a prediction method which is able to prognosticate the next driving-behavioral state. This method has been constructed based on the unsupervised double articulation analyzer method (DAA) which is able to segment meaningless continuous driving-behavioral data into a meaningful sequence of driving situations. Thereafter, our novel model by mining the sequences of driving situations can …