Computer Engineering Commons^™

On Designing An Ecg-Based Intelligent System: Utilizing The Heart’S Electrical Activity To Recognize Humans And Detect Arrhythmia, Sara Saeed Abdeldayem

Graduate Theses, Dissertations, and Problem Reports (ETD)

The electrocardiogram (ECG) signal is the bioelectrical signal that reflects the heart's activity. It has been extensively used as a diagnostic tool since it holds information about the cardiac health condition. However, recent researches have shown that it exhibits an inter-subject variability property. Therefore, it can be used as a biometric-based modality for either identification or verification purposes. Nevertheless, some of the challenges are faced while employing such a signal. For instance, ECG signal is prone to noise, accordingly, noise filters should be designed to remove the noise while keeping the signal properties. Moreover, factors such as medications, health condition, …

Enhancing Cognitive Algorithms For Optimal Performance Of Adaptive Networks, Hector Lugo-Cordero

Electronic Theses and Dissertations

This research proposes to enhance some Evolutionary Algorithms in order to obtain optimal and adaptive network configurations. Due to the richness in technologies, low cost, and application usages, we consider Heterogeneous Wireless Mesh Networks. In particular, we evaluate the domains of Network Deployment, Smart Grids/Homes, and Intrusion Detection Systems. Having an adaptive network as one of the goals, we consider a robust noise tolerant methodology that can quickly react to changes in the environment. Furthermore, the diversity of the performance objectives considered (e.g., power, coverage, anonymity, etc.) makes the objective function non-continuous and therefore not have a derivative. For these …

Energy Efficient And Secure Wireless Sensor Networks Design, Afraa Attiah

Electronic Theses and Dissertations

Wireless Sensor Networks (WSNs) are emerging technologies that have the ability to sense, process, communicate, and transmit information to a destination, and they are expected to have significant impact on the efficiency of many applications in various fields. The resource constraint such as limited battery power, is the greatest challenge in WSNs design as it affects the lifetime and performance of the network. An energy efficient, secure, and trustworthy system is vital when a WSN involves highly sensitive information. Thus, it is critical to design mechanisms that are energy efficient and secure while at the same time maintaining the desired …

Joint Optimization Of Illumination And Communication For A Multi-Element Vlc Architecture, Sifat Ibne Mushfique

Electronic Theses and Dissertations

Because of the ever increasing demand wireless data in the modern era, the Radio Frequency (RF) spectrum is becoming more congested. The remaining RF spectrum is being shrunk at a very heavy rate, and spectral management is becoming more difficult. Mobile data is estimated to grow more than 10 times between 2013 and 2019, and due to this explosion in data usage, mobile operators are having serious concerns focusing on public Wireless Fidelity (Wi-Fi) and other alternative technologies. Visible Light Communication (VLC) is a recent promising technology complementary to RF spectrum which operates at the visible light spectrum band (roughly …

Bridging The Gap Between Application And Solid-State-Drives, Jian Zhou

Electronic Theses and Dissertations

Data storage is one of the important and often critical parts of the computing system in terms of performance, cost, reliability, and energy. Numerous new memory technologies, such as NAND flash, phase change memory (PCM), magnetic RAM (STT-RAM) and Memristor, have emerged recently. Many of them have already entered the production system. Traditional storage optimization and caching algorithms are far from optimal because storage I/Os do not show simple locality. To provide optimal storage we need accurate predictions of I/O behavior. However, the workloads are increasingly dynamic and diverse, making the long and short time I/O prediction challenge. Because of …

Real-Time Sil Emulation Architecture For Cooperative Automated Vehicles, Nitish Gupta

Electronic Theses and Dissertations

This thesis presents a robust, flexible and real-time architecture for Software-in-the-Loop (SIL) testing of connected vehicle safety applications. Emerging connected and automated vehicles (CAV) use sensing, communication and computing technologies in the design of a host of new safety applications. Testing and verification of these applications is a major concern for the automotive industry. The CAV safety applications work by sharing their state and movement information over wireless communication links. Vehicular communication has fueled the development of various Cooperative Vehicle Safety (CVS) applications. Development of safety applications for CAV requires testing in many different scenarios. However, the recreation of test …

A Game-Theoretic Model For Regulating Freeriding In Subsidy-Based Pervasive Spectrum Sharing Markets, Mostafizur Rahman

Electronic Theses and Dissertations

Cellular spectrum is a limited natural resource becoming scarcer at a worrisome rate. To satisfy users' expectation from wireless data services, researchers and practitioners recognized the necessity of more utilization and pervasive sharing of the spectrum. Though scarce, spectrum is underutilized in some areas or within certain operating hours due to the lack of appropriate regulatory policies, static allocation and emerging business challenges. Thus, finding ways to improve the utilization of this resource to make sharing more pervasive is of great importance. There already exists a number of solutions to increase spectrum utilization via increased sharing. Dynamic Spectrum Access (DSA) …

An Entropy-Histogram Approach For Image Similarity And Face Recognition, Mohammed Aljanabi, Zahir Hussain, Song F. Lu

Research outputs 2014 to 2021

Image similarity and image recognition are modern and rapidly growing technologies because of their wide use in the field of digital image processing. It is possible to recognize the face image of a specific person by finding the similarity between the images of the same person face and this is what we will address in detail in this paper. In this paper, we designed two new measures for image similarity and image recognition simultaneously. The proposed measures are based mainly on a combination of information theory and joint histogram. Information theory has a high capability to predict the relationship between …

Handwritten Bangla Character Recognition Using The State-Of-The-Art Deep Convolutional Neural Networks, Md Zahangir Alom, Paheding Sidike, Mahmudul Hasan, Tarek M. Taha, Vijayan K. Asari

Electrical and Computer Engineering Faculty Publications

In spite of advances in object recognition technology, handwritten Bangla character recognition (HBCR) remains largely unsolved due to the presence of many ambiguous handwritten characters and excessively cursive Bangla handwritings. Even many advanced existing methods do not lead to satisfactory performance in practice that related to HBCR. In this paper, a set of the state-of-the-art deep convolutional neural networks (DCNNs) is discussed and their performance on the application of HBCR is systematically evaluated. The main advantage of DCNN approaches is that they can extract discriminative features from raw data and represent them with a high degree of invariance to object …

Field-Verified Integrated Eaf-Svc-Electrode Positioning Model Simulation And Anovel Hybrid Series Compensation Control For Eaf, Ahmed Hassan, Amr Abou-Ghazala, Ashraf Megahed

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, modeling and simulation of a typical steel-making network are realized using MATLAB Simulink environment and validated using trends collected from an actual steel plant. The models integrate different reactions between an electric arc furnace (EAF), a static Var compensator, and electrode positioning systems according to a previously introduced theory of operations. In addition, the conventional electrode positioning control performance is compared with the new hybrid series compensation control method to demonstrate the superiority of the new method regarding system response. With help of the proposed series compensation, the reference resistance value was restored 2.5 s faster than …

A Novel Efficient Tsv Built-In Test For Stacked 3d Ics, Badi Guibane, Belgacem Hamdi, Brahim Ben Salem, Abdellatif Mtibaa

Turkish Journal of Electrical Engineering and Computer Sciences

A through-silicon via (TSV) is established as the main enabler for a three-dimensional integrated circuit (3D IC) that increases system density and compactness. The exponential increase in TSV density led to TSV-induced catastrophic and parametric faults. We propose an original architecture that detects errors caused by TSV manufacturing defects. The proposed design for testability is a built-in technique that detects errors in an early manufacturing stage and is hence very economically attractive. The proposal is capable of testing each and every TSV in the network. The technique achieves high fault coverage and high observability.

Sar Image Denoising Based On Patch Ordering In Nonsubsample Shearlet Domain, Shuaiqi Liu, Qi Hu, Pengfei Li, Jie Zhao, Zhihui Zhu

Turkish Journal of Electrical Engineering and Computer Sciences

Synthetic aperture radar (SAR) has been extensively adopted in a variety of fields, e.g., agriculture and marine fields. In this regard, the improvement of SAR image quality has aroused a wide concern worldwide. In recent years, image processing based on local patches has been very popular and proven feasible. In this paper, a novel SAR image denoising algorithm is proposed in the NSST domain on the basis of patch ordering. First, the shearlet transform is applied to logarithmic transformation of the noisy SAR image. Second, the coefficients of the shearlet are denoised respectively by combining patch ordering and 1D filtering. …

The Influence Of Employee's Perceptions Of Top Management Support, Information Technology Competence, Technology Strategy, Organizational Climate, And Organization's Nationality On Information Systems Security, And Quality Success, Saud Fawwaz Alsahli

Master's Theses and Doctoral Dissertations

This research investigated the relationship between levels of top management, information technology, competence, technology strategy, and organizational climate within an organization and organization’s success of information systems. The study conducted a quantitative study of 120 employees working for two organizations within the Ministry of Interior in Saudi Arabia and determine whether organizational attributes affect changes in information systems success. In addition, an American organization (40 employees) was used as a comparison. The analysis of the study found that top management support, information technology competence, technology strategy, and organizational climate were strongly correlated with information systems success in Saudi organizations. In …

Retail Data Analytics Using Graph Database, Rashmi Priya

Theses and Dissertations--Computer Science

Big data is an area focused on storing, processing and visualizing huge amount of data. Today data is growing faster than ever before. We need to find the right tools and applications and build an environment that can help us to obtain valuable insights from the data. Retail is one of the domains that collects huge amount of transaction data everyday. Retailers need to understand their customer’s purchasing pattern and behavior in order to take better business decisions.

Market basket analysis is a field in data mining, that is focused on discovering patterns in retail’s transaction data. Our goal is …

Interactive Clinical Event Pattern Mining And Visualization Using Insurance Claims Data, Zhenhui Piao

Theses and Dissertations--Computer Science

With exponential growth on a daily basis, there is potentially valuable information hidden in complex electronic medical records (EMR) systems. In this thesis, several efficient data mining algorithms were explored to discover hidden knowledge in insurance claims data. The first aim was to cluster three levels of information overload(IO) groups among chronic rheumatic disease (CRD) patient groups based on their clinical events extracted from insurance claims data. The second aim was to discover hidden patterns using three renowned pattern mining algorithms: Apriori, frequent pattern growth(FP-Growth), and sequential pattern discovery using equivalence classes(SPADE). The SPADE algorithm was found to be the …

“Woodlands” - A Virtual Reality Serious Game Supporting Learning Of Practical Road Safety Skills., Krzysztof Szczurowski, Matt Smith

Conference Papers

In developed societies road safety skills are taught early and often practiced under the supervision of a parent, providing children with a combination of theoretical and practical knowledge. At some point children will attempt to cross a road unsupervised, at that point in time their safety depends on the effectiveness of their road safety education. To date, various attempts to supplement road safety education with technology were made. Most common approach focus on addressing declarative knowledge, by delivering road safety theory in an engaging fashion. Apart from expanding on text based resources to include instructional videos and animations, some stakeholders …

Mintbase V2.0: A Comprehensive Database For Trna-Derived Fragments That Includes Nuclear And Mitochondrial Fragments From All The Cancer Genome Atlas Projects., Venetia Pliatsika, Phillipe Loher, Rogan Magee, Aristeidis G. Telonis, Eric R. Londin, Megumi Shigematsu, Yohei Kirino, Isidore Rigoutsos

Computational Medicine Center Faculty Papers

MINTbase is a repository that comprises nuclear and mitochondrial tRNA-derived fragments ('tRFs') found in multiple human tissues. The original version of MINTbase comprised tRFs obtained from 768 transcriptomic datasets. We used our deterministic and exhaustive tRF mining pipeline to process all of The Cancer Genome Atlas datasets (TCGA). We identified 23 413 tRFs with abundance of ≥ 1.0 reads-per-million (RPM). To facilitate further studies of tRFs by the community, we just released version 2.0 of MINTbase that contains information about 26 531 distinct human tRFs from 11 719 human datasets as of October 2017. Key new elements include: the ability …

On The Security And Quality Of Wireless Communications In Outdoor Mobile Environment, Sharaf J. Malebary

Theses and Dissertations

The rapid advancement in wireless technology along with their low cost and ease of deployment have been attracting researchers academically and commercially. Researchers from private and public sectors are investing into enhancing the reliability, robustness, and security of radio frequency (RF) communications to accommodate the demand and enhance lifestyle. RF base communications -by nature- are slower and more exposed to attacks than a wired base (LAN). Deploying such networks in various cutting-edge mobile platforms (e.g. VANET, IoT, Autonomous robots) adds new challenges that impact the quality directly. Moreover, adopting such networks in public outdoor areas make them vulnerable to various …

Bytecode-Based Multiple Condition Coverage: An Initial Investigation, Srujana Bollina

Theses and Dissertations

Masking occurs when one condition prevents another condition from influencing the output of a Boolean expression. Logic-based adequacy criteria such as Multiple Condition Coverage (MCC) are designed to overcome masking at the within-expression level, but can offer no guarantees about masking in subsequent expressions. As a result, a Boolean expression written as a single complex statement will yield test cases that are more likely to overcome masking than when the expression is written as series of simple statements. Many approaches to automated analysis and test case generation for Java systems operate not on the source code representation of code, but …

Information Systems Continuance: Faculty Perceptions Of Canvas, Leila Halawi, Richard V. Mccarthy, James Farah

Publications

This research in process proposes a study of the theoretical background, motivation, and methods to examine factors that predict faculty perceived usefulness, ease of use, satisfaction and learning facilitation of the Canvas learning management system. We propose utilizing a model to predict continuance intention of use in an online class using Canvas and describe the methodology that will be used to test this model. The focus of this research is on continuance usage as it is the variable that enhances the teaching and learning experience with factors that are helpful in understanding the phenomenon.

An Application Of Natural Language Processing For Triangulation Of Cognitive Load Assessments In Third Level Education, Luis Alfredo Contreras

Dissertations

Work has been done to measure Mental Workload based on applications mainly related to ergonomics, human factors, and Machine Learning. The influence of Machine Learning is a reflection of an increased use of new technologies applied to areas conventionally dominated by theoretical approaches. However, collaboration between MWL and Natural Language Processing techniques seems to happen rarely. In this sense, the objective of this research is to make use of Natural Languages Processing techniques to contribute to the analysis of the relationship between Mental Workload subjective measures and Relative Frequency Ratios of keywords gathered during pre-tasks and post-tasks of MWL activities …

Can Threshold-Based Sensor Alerts Be Analysed To Detect Faults In A District Heating Network?, Liam Cantwell

Dissertations

Older IoT “smart sensors” create system alerts from threshold rules on reading values. These simple thresholds are not very flexible to changes in the network. Due to the large number of false positives generated, these alerts are often ignored by network operators. Current state-of-the-art analytical models typically create alerts using raw sensor readings as the primary input. However, as greater numbers of sensors are being deployed, the growth in the number of readings that must be processed becomes problematic. The number of analytic models deployed to each of these systems is also increasing as analysis is broadened. This study aims …

Elasticity Measurement In Caas Environments - Extending The Existing Bungee Elasticity Benchmark To Aws's Elastic Container Service, Nora Limbourg

Dissertations

Rapid elasticity and automatic scaling are core concepts of most current cloud computing systems. Elasticity describes how well and how fast cloud systems adapt to increases and decreases in workload. In parallel, software architectures are moving towards employing containerised microservices running on systems managed by container orchestration platforms. Cloud users who employ such container-based systems may want to compare the elasticity of different systems or system settings to ensure rapid elasticity and maintain service level objectives while avoiding over-provisioning. Previous research has established a variety of metrics to measure elasticity. Some existing benchmark tools are designed to measure elasticity in …

Use Of Hyperspectral Images (Hsi) And Convolutional Neural Network (Cnn) To Identify Normal, Precancerous And Cancerous Tissues, Pallavi Jain

Dissertations

Cancer detection has been a great topic of research for a long time, as early detection of cancer can help in increasing the survival rate of patients by providing on time better treatment. A robust system is required in order to detect early-stage cancer as its difficult to identify early-stage cancer from the normal clinical process. The computer vision techniques provide a new way to understand the challenges related to the medical image analysis. This thesis presents the medical image analysis using a combination of Convolutional Neural Network and Hyperspectral Images of cancer patient's tissues. The idea behind choosing the …

From Business Understanding To Deployment: An Application Of Machine Learning Algorithms To Forecast Customer Visits Per Hour To A Fast-Casual Restaurant In Dublin, Odunayo David Adedeji

Dissertations

This research project identifies the significant factors that affects the number of customer visits to a fast-casual restaurant every hour and proceeds to develop several machine learning models to forecast customer visits. The core value proposition of fast-casual restaurants is quality food delivered at speed which means they have to prepare meals in advance of customers visit but the problem with this approach is in forecasting future demand, under estimating demand could lead to inadequate meal preparation which would leave customers unsatisfied while over estimation of demand could lead to wastage especially with restaurants having to comply with food safety …

Can Machine Learning Beat Physics At Modeling Car Crashes?, Gavin Byrne

Dissertations

This study aimed to look at a traditional method used for measuring the severity and principle direction of force of a car crash and see if it could be improved on using machine learning models. The data used was publicly available from the NHTSA database and included descriptions of the vehicle, test and sensors as well as the accelerometer data over the period of the crashes. The models built were SVM classifiers and multinomial regression models. Although the SVM and Regression models were built successfully and gave higher levels of accuracy than the momentum models in terms of the severity, …

Supervised Learning Models To Predict Stock Direction Within Different Sectors In A Bull And Bear Market, Tiffany Razy

Dissertations

Forecasting stock market price movement is a well researched and an alluring topic within the machine learning and financial realm. Supervised machine learning algorithms such as Random Forest (RF) and Support Vector Machines (SVM) have been used independently to gain insight on the market. With such volatility in the market the scope of this study will utilized the RF and SVM in a very volatility market to determine if these models will perform at a high level or outperform each other in both markets. This relative study is performed on 16 stocks in 4 different sectors over the bear market …

Identifying Expert Investors On Financial Microblog Via Artificial Neural Networks, Pierluca Del Buono

Dissertations

In the recent years, thanks to social media platform, a plethora of information has been available to financial investors, that were traditionally dependent from financial institutions advisors. Strategies are now shared among web users, performances of stocks are commented in web communities and hints and suggestions are travelling on the internet with a fast pace, in a way that was unthinkable few years before. Several attempts have been made in the recent past, to predict Market movements and trends from activity of Financial Social Networks participants, and to evaluate if contributions from individuals with high level of expertise distinguish themselves …

Forecasting Changes In Religiosity And Existential Security With An Agent-Based Model, Ross J. Gore, Carlos Lemos, F. Leron Shults, Wesley J. Wildman

VMASC Publications

We employ existing data sets and agent-based modeling to forecast changes in religiosity and existential security among a collective of individuals over time. Existential security reflects the extent of economic, socioeconomic and human development provided by society. Our model includes agents in social networks interacting with one another based on the education level of the agents, the religious practices of the agents, and each agent's existential security within their natural and social environments. The data used to inform the values and relationships among these variables is based on rigorous statistical analysis of the International Social Survey Programme Religion Module (ISSP) …

Ontology-Guided Pre-Release Inference Disruption, Mark Stephen Daniels

Theses and Dissertations

We investigate privacy violations occurring when non-confidential patient data is combined with medical domain ontologies to disclose a patient’s protected health information (PHI). We propose a framework that detects privacy violations and eliminates undesired inferences. Our inference channel removal process is based on controlling the release of the data items that lead to undesired inferences. These data items are either blocked from release or generalized to eliminate the disclosure of the PHI. We show that our method is sound and complete. Soundness means the only inference paths generated logically follow from released data and corresponding domain knowledge. Completeness means we …