Digital Commons Network^™

A Control-Theoretic Design And Analysis Framework For Resilient Hard Real-Time Systems, Pradeep Mahendra Hettiarachchi

Wayne State University Dissertations

We introduce a new design metric called system-resiliency which characterizes the maximum unpredictable

external stresses that any hard-real-time performance mode can withstand. Our proposed systemresiliency

framework addresses resiliency determination for real-time systems with physical and hardware

limitations. Furthermore, our framework advises the system designer about the feasible trade-offs between

external system resources for the system operating modes on a real-time system that operates in a

multi-parametric resiliency environment.

Modern multi-modal real-time systems degrade the system’s operational modes as a response to unpredictable

external stimuli. During these mode transitions, real-time systems should demonstrate a reliable

and graceful degradation of service. Many …

Resource Management In Cloud And Big Data Systems, Lena Mashayekhy

Wayne State University Dissertations

Cloud computing is a paradigm shift in computing, where services are offered and acquired on demand in a cost-effective way. These services are often virtualized, and they can handle the computing needs of big data analytics. The ever-growing demand for cloud services arises in many areas including healthcare, transportation, energy systems, and manufacturing. However, cloud resources such as computing power, storage, energy, dollars for infrastructure, and dollars for operations, are limited. Effective use of the existing resources raises several fundamental challenges that place the cloud resource management at the heart of the cloud providers' decision-making process. One of these challenges …

Object Tracking: Appearance Modeling And Feature Learning, Raed Almomani

Wayne State University Dissertations

Object tracking in real scenes is an important problem in computer vision due to increasing usage of tracking systems day in and day out in various applications such as surveillance, security, monitoring and robotic vision. Object tracking is the process of locating objects of interest in every frame of video frames. Many systems have been proposed to address the tracking problem where the major challenges come from handling appearance variation during tracking caused by changing scale, pose, rotation, illumination and occlusion.

In this dissertation, we address these challenges by introducing several novel tracking techniques. First, we developed a multiple object …

A Prediction Modeling Framework For Noisy Welding Quality Data, Junheung Park

Wayne State University Dissertations

Numerous and various research projects have been conducted to utilize historical manufacturing process data in product design. These manufacturing process data often contain data inconsistencies, and it causes challenges in extracting useful information from the data. In resistance spot welding (RSW), data inconsistency is a well-known issue. In general, such inconsistent data are treated as noise data and removed from the original dataset before conducting analyses or constructing prediction models. This may not be desirable for every design and manufacturing applications since every data can contain important information to further explain the process. In this research, we propose a prediction …

Semantic Web Based Relational Database Access With Conflict Resolution, Fayez Khazalah

Wayne State University Dissertations

This thesis focuses on (1) accessing relational databases through Semantic Web technologies and (2) resolving conflicts that usually arises when integrating data from heterogeneous source schemas and/or instances.

In the first part of the thesis, we present an approach to access relational databases using Semantic Web technologies. Our approach is built on top of Ontop framework for Ontology Based Data Access. It extracts both Ontop mappings and an equivalent OWL ontology from an existing database schema. The end users can then access the underlying data source through SPARQL queries. The proposed approach takes into consideration the different relationships between the …

Efficient Synergistic De Novo Co-Assembly Of Bacterial Genomes From Single Cells Using Colored De Bruijn Graph, Narjes Sadat Movahedi Tabrizi

Wayne State University Dissertations

Recent progress in DNA amplification techniques, particularly multiple displacement

amplification (MDA), has made it possible to sequence and assemble bacterial

genomes from a single cell. However, the quality of single cell genome assembly has

not yet reached the quality of normal multi-cell genome assembly due to the coverage

bias (including uneven depth of coverage and region blackout) and errors caused by

MDA. Computational methods try to mitigates the amplification bias. In this document

we introduce a de novo co-assembly method using colored de Bruijn graph,

which can overcome the problem of blackout regions due to amplification bias. The

algorithm is …

Efficient Algorithms And Optimizations For Scientific Computing On Many-Core Processors, Kamel Rushaidat

Wayne State University Dissertations

Designing efficient algorithms for many-core and multicore architectures requires using different strategies to allow for the best exploitation of the hardware resources on those architectures. Researchers have ported many scientific applications to modern many-core and multicore parallel architectures, and by doing so they have achieved significant speedups over running on single CPU cores. While many applications have achieved significant speedups, some applications still require more effort to accelerate due to their inherently serial behavior.

One class of applications that has this serial behavior is the Monte Carlo simulations. Monte Carlo simulations have been used to simulate many problems in statistical …

Shape Analysis Using Spectral Geometry, Jiaxi Hu

Wayne State University Dissertations

Shape analysis is a fundamental research topic in computer graphics and computer vision. To date, more and more 3D data is produced by those advanced acquisition capture devices, e.g., laser scanners, depth cameras, and CT/MRI scanners. The increasing data demands advanced analysis tools including shape matching, retrieval, deformation, etc. Nevertheless, 3D Shapes are represented with Euclidean transformations such as translation, scaling, and rotation and digital mesh representations are irregularly sampled. The shape can also deform non-linearly and the sampling may vary. In order to address these challenging problems, we investigate Laplace-Beltrami shape spectra from the differential geometry perspective, focusing more …

Predictable Real-Time Wireless Networking For Sensing And Control, Xiaohui Liu

Wayne State University Dissertations

Towards the end goal of providing predictable real-time wireless networking for sensing and control, we have developed a real-time routing protocol MTA that predictably delivers data by their deadlines, and a scheduling protocol PRKS to ensure a certain link reliability based on the Physical-ratio-K (PRK) model, which is both realistic and amenable for distributed implementation, and a greedy scheduling algorithm to deliver as many packets as possible to the sink by a deadline in lossy multi-hop wireless sensor networks.

Real-time routing is a basic element of closed-loop, real-time sensing and control, but it is challenging due to dynamic, uncertain link/path …

Evolving Heterogeneous And Subcultured Social Networks For Optimization Problem Solving In Cultural Algorithms, Yousof Gawasmeh

Wayne State University Dissertations

Cultural Algorithms are computational models of social evolution based upon principle of Cultural Evolution. A Cultural Algorithm are composed of a Belief Space consisting of a network of active and passive knowledge sources and a Population Space of agents. The agents are connected via a social fabric over which information used in agent problem solving is passed. The knowledge sources in the Belief Space compete with each other in order to influence the decision making of agents in the Population Space. Likewise, the problem solving experiences of agents in the Population Space are sent back to the Belief Space and …

Applications Of Machine Learning In Biology And Medicine, Saied Haidarian Shahri

Wayne State University Dissertations

Machine learning as a field is defined to be the set of computational algorithms that improve their performance by assimilating data.

As such, the field as a whole has found applications in many diverse disciplines from robotics and communication in engineering to economics and finance, and also biology and medicine.

It should not come as a surprise that many popular methods in use today have completely different origins.

Despite this heterogeneity, different methods can be divided into standard tasks, such as supervised, unsupervised, semi-supervised and reinforcement learning.

Although machine learning as a field can be formalized as methods trying to …

Building Computing-As-A-Service Mobile Cloud System, Kun Wang

Wayne State University Dissertations

The last five years have witnessed the proliferation of smart mobile devices, the explosion of various mobile applications and the rapid adoption of cloud computing in business, governmental and educational IT deployment. There is also a growing trends of combining mobile computing and cloud computing as a new popular computing paradigm nowadays. This thesis envisions the future of mobile computing which is primarily affected by following three trends: First, servers in cloud equipped with high speed multi-core technology have been the main stream today. Meanwhile, ARM processor powered servers is growingly became popular recently and the virtualization on ARM systems …

Novel Incorportation Of Biomedical Engineering Algorithms (Bispectral Index Guided Or Anesthetic Concentration Guided) In Real-Time Decision Support To Prevent Intraoperative Awareness Using An Electronic Anesthesia Information Mananagement System, Amy Melanie Shanks

Wayne State University Dissertations

Background: Intraoperative awareness with explicit recall (AWR) is a feared complication of surgery that can lead to significant psychological distress. Several large prospective trials have been completed comparing two methods of monitoring anesthetic depth [minimum alveolar concentration (MAC) or electroencephalography (EEG) monitoring using the bispectral index (BIS)] for the prevention of AWR. However, these trials were conducted in high risk populations, limiting generalizability.

Research Hypothesis: Real-time decision support with Anesthesia Information Management System alerts based on a novel anesthetic concentration algorithm (incorporating the use of intravenous anesthetics) or an EEG-guided algorithm will reduce the known incidence of AWR.

Methods: First, …