Computer Engineering Commons^™

Using Moocs To Promote Digital Accessibility And Universal Design, The Moocap Experience, John Gilligan, W. Chen, J. Darzentas

Articles

The recently completed Massive Open Online Course for Accessibility Partnership project (MOOCAP), had the twin aims of establishing a strategic partnership around the promotion of Universal Design and Accessibility for ICT professionals and of developing a suite of Open Educational resources (OERs) in this domain. MOOCAP's eight university partners from Germany, Norway, Greece, Ireland, the UK and Austria have a significant history in developing and providing courses in the domains of Universal Design and Accessibility, as well as leading research and advocacy roles within Europe. The MOOCAP project consisted of two phases: the development of an introductory MOOC on Digital …

Examining The Effects Of A Virtual Character On Learning And Engagement In Serious Games, Vihanga Gamage, Cathy Ennis

Articles

Virtual characters have been employed for many purposes including interacting with players of serious games, with a purpose to increase engagement. These characters are often embodied conversational agents playing diverse roles, such as demonstrators, guides, teachers or interviewers. Recently, much research has been conducted into properties that affect the realism and plausibility of virtual characters, but it is less clear whether the inclusion of interactive agents in serious applications can enhance a user’s engagement with the application, or indeed increase efficacy. In a first step towards answering these questions, we conducted a study where a Virtual Learning Environment was used …

Hardware Implementation And Comparison Of Displacement Retrieval Algorithms For A Laser Diode-Based Optical Feedback Interferometric Sensor, Ghazi Rehman, Usman Zabit, Muhammad Obaid Ullah

Turkish Journal of Electrical Engineering and Computer Sciences

Optical feedback interferometer (OFI) lasers, also called self-mixing (SM) lasers, have been widely explored over the last couple of decades due to their low cost, compactness, and self-aligned nature and they provide a very good solution for measurements of displacement, vibration, distance, velocity, etc. The SM effect takes place when a part of the laser beam is fed back to the active laser cavity after reflecting from the target. The reflected beam interferes with the emitted beam and hence the optical and spectral characteristics of the laser get changed. To retrieve the vibration or displacement signal of the target from …

A Graphical File System Visualization Tool For Operating Systems, Jacob Bundt

Honors Program Theses

Fileshark was developed for the primary purpose of aiding in the teaching of students in file system related courses, particularly at the undergraduate level, by providing a visual representation of often abstract file system ideas. Many teachers find it difficult to teach the file system in detail in a typical operating systems classroom environment due to its abstract nature and the inability to truly observe what is occurring within the file system. Fileshark provides an easy to use interface that can be used by both students and professors to visualize the internal workings of the file system and the processes …

Characterization Of Language Cortex Activity During Speech Production And Perception, Hassan Baker

Electrical & Computer Engineering Theses & Dissertations

Millions of people around the world suffer from severe neuromuscular disorders such as spinal cord injury, cerebral palsy, amyotrophic lateral sclerosis (ALS), and others. Many of these individuals cannot perform daily tasks without assistance and depend on caregivers, which adversely impacts their quality of life. A Brain-Computer Interface (BCI) is technology that aims to give these people the ability to interact with their environment and communicate with the outside world. Many recent studies have attempted to decode spoken and imagined speech directly from brain signals toward the development of a natural-speech BCI. However, the current progress has not reached practical …

Coexistence And Secure Communication In Wireless Networks, Saygin Bakşi

Electrical & Computer Engineering Theses & Dissertations

In a wireless system, transmitted electromagnetic waves can propagate in all directions and can be received by other users in the system. The signals received by unintended receivers pose two problems; increased interference causing lower system throughput or successful decoding of the information which removes secrecy of the communication. Radio frequency spectrum is a scarce resource and it is allocated by technologies already in use. As a result, many communication systems use the spectrum opportunistically whenever it is available in cognitive radio setting or use unlicensed bands. Hence, efficient use of spectrum by sharing users is crucial to increase maximize …

Deep Recurrent Learning For Efficient Image Recognition Using Small Data, Mahbubul Alam

Electrical & Computer Engineering Theses & Dissertations

Recognition is fundamental yet open and challenging problem in computer vision. Recognition involves the detection and interpretation of complex shapes of objects or persons from previous encounters or knowledge. Biological systems are considered as the most powerful, robust and generalized recognition models. The recent success of learning based mathematical models known as artificial neural networks, especially deep neural networks, have propelled researchers to utilize such architectures for developing bio-inspired computational recognition models. However, the computational complexity of these models increases proportionally to the challenges posed by the recognition problem, and more importantly, these models require a large amount of data …

Applying Machine Learning To Advance Cyber Security: Network Based Intrusion Detection Systems, Hassan Hadi Latheeth Al-Maksousy

Computer Science Theses & Dissertations

Many new devices, such as phones and tablets as well as traditional computer systems, rely on wireless connections to the Internet and are susceptible to attacks. Two important types of attacks are the use of malware and exploiting Internet protocol vulnerabilities in devices and network systems. These attacks form a threat on many levels and therefore any approach to dealing with these nefarious attacks will take several methods to counter. In this research, we utilize machine learning to detect and classify malware, visualize, detect and classify worms, as well as detect deauthentication attacks, a form of Denial of Service (DoS). …

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.

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 …

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 …

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, …