Computer Engineering Commons^™

A New Approach To Linear Displacement Measurements Based On Hall Effect Sensors, İsmai̇l Yari̇çi̇, Yavuz Öztürk

Turkish Journal of Electrical Engineering and Computer Sciences

Since displacement is a vital variable to be considered in many industrial applications, displacement sensing devices have been extensively studied both theoretically and experimentally. There have been also many studies on Hall effect-based displacement measurement, but for many systems linearity still remains a problem. This paper discusses different approaches to calculate the magnetic field due to a cylindrical permanent magnet and proposes a new setup geometry with 2-Hall effect sensors and a permanent magnet between them to overcome the linearity problems. Furthermore, theoretical and experimental studies of the discussed displacement sensor were presented by focusing on the linear range and …

Adversarial Training Of Deep Neural Networks, Anabetsy Termini

CCAC Theses and Dissertations

Deep neural networks used for image classification are highly susceptible to adversarial attacks. The de facto method to increase adversarial robustness is to train neural networks with a mixture of adversarial images and unperturbed images. However, this method leads to robust overfitting, where the network primarily learns to recognize one specific type of attack used to generate the images while remaining vulnerable to others after training. In this dissertation, we performed a rigorous study to understand whether combinations of state of the art data augmentation methods with Stochastic Weight Averaging improve adversarial robustness and diminish adversarial overfitting across a wide …

Improving The Performance, Energy Efficiency And Security Of Gpus, Xin Wang

Theses and Dissertations

The work in this dissertation achieves to enhance the performance, energy-efficiency, and security of the GPUs. We noticed that, as the demand of hardware resources keeps rising in GPUs, the energy consumption becomes unaffordable and places barriers for further performance boost. To resolve this issue, we have proposed several novel GPU micro-architectures that are able to assist the GPUs to execute in an energy-efficient manner. They also provide the potential for further performance enhancement in GPUs. Firstly, we proposed a GPU register packing scheme that stores multiple narrow-width operands to a single register to save register file resources. The unoccupied …

Improving The Flexibility And Robustness Of Machine Tending Mobile Robots, Richard Ethan Hollingsworth

Theses and Dissertations

While traditional manufacturing production cells consist of a fixed base robot repetitively performing tasks, the Industry 5.0 flexible manufacturing cell (FMC) aims to bring Autonomous Industrial Mobile Manipulators (AIMMs) to the factory floor. Composed of a wheeled base and a robot arm, these collaborative robots (cobots) operate alongside people while autonomously performing tasks at different workstations. AIMMs have been tested in real production systems, but the development of the control algorithms necessary for automating a robot that is a combination of two cobots remains an open challenge before the large scale adoption of this technology occurs in industry. Currently popular …

Optimising Electric Vehicle Charging Infrastructure In Dublin Using Geecharge, Alexander Mutua Mutiso, Ruairí De Fréin, Ali Malik, Eliel Kibanza, Marco Sahbane, Maxime Pantel

Conference papers

Range anxiety poses a hurdle to the adoption of Electric Vehicles (EVs), as drivers worry about running out of charge without timely access to a Charging Point (CP). We present novel methods for optimising the distribution of CPs, namely, EV portacharge and GEECharge. These solutions distribute CPs in Dublin, in this paper, by considering the population density and Points Of Interest (POIs) or road traffic. The object of this paper is to (1) develop and evaluate methods to distribute CPs in Dublin city; (2) optimise CP allocation; (3) visualise paths in the graph network to show the most used roads …

An Assessment Of The Effectiveness Of Using Data Analytics To Predict Death Claim Seasonality And Protection Policy Review Lapses In A Life Insurance Company, Jennifer Loftus

ICT

Data analytics tools are becoming increasingly common in the life insurance industry. This research considers two use cases for predictive analytics in a life insurance company based in Ireland. The first case study relates to the use of time series models to forecast the seasonality of death claim notifications. The baseline model predicted no seasonal variation in death claim notifications over a calendar year. This reflects the life insurance company’s current approach, whereby it is assumed that claims are notified linearly over a calendar year. More accurate forecasting of death claims seasonality would enhance the life insurance company’s cashflow planning …

The Role Of Data Analytics To Address Water Stress In Africa, Khalil Beladda

ICT

Water stress, a global concern transcending geographical boundaries, significantly impacts the African continent. Affecting one in three people in Africa, sustainable water management is imperative for ecological and human welfare. This research emphasizes the pivotal role of data analytics in addressing water stress challenges in Africa and beyond.

Recurrent Neural Networks For Flash Gdp Estimates In Ireland: A Comparison With Traditional Econometric Methods, Justin Flannery

ICT

GDP is the single most important barometer for the health of an economy. It’s an important input into the decision making processes of government, industry and state institutions such as central banks. To be useful as an indicator, GDP estimates need to be both timely and accurate. To meet the needs of users, many national statistical institutes publish early or flash estimates of GDP which are produced within 30 days after the end of a quarter. Given the long lags involved in the data collection processes which feed into GDP estimates, these flash estimates are often largely model based. Within …

Pronostic Of Colo-Rectal Cancer (Crc) Using Machine Learning Models On Organoids Derived Of Patient, Claudia Andrea Leiva Acevedo

ICT

Colorectal Cancer (CRC) is a globally prevalent and deadly carcinoma, necessitating advanced treatment approaches. Despite ongoing advancements, the mortality rate remains high. Various biological models, including animal studies, cell lines, and the emerging organoid model, contribute to understanding molecular mechanisms. Organoids, 3D cultures derived from tumor epithelial cells, offer advantages such as enhanced diversity, genetic modification, and extended culture capabilities. Recent applications of machine learning (ML) in predicting CRC treatment responses using organoids and tissue data indicate a promising avenue for advancing personalized therapies.

Unlocking The Pragmatics Of Emoji: Evaluation Of The Integration Of Pragmatic Markers For Sarcasm Detection, Niamh Farnham

ICT

Emojis have become an integral element of online communications, serving as a powerful, under-utilised resource for enhancing pragmatic understanding in NLP. Previous works have highlighted their potential for improvement of more complex tasks such as the identification of figurative literary devices including sarcasm due to their role in conveying tone within text. However present state-of-the-art does not include the consideration of emoji or adequately address sarcastic markers such as sentiment incongruence. This work aims to integrate these concepts to generate more robust solutions for sarcasm detection leveraging enhanced pragmatic features from both emoji and text tokens. This was achieved by …

Evaluating The Potential Of Ensemble Learning For One Day-Ahead Forecasting Of Power System Demand In Ireland, Karol Skowronski

ICT

Accurate One Day-Ahead Demand Forecasting (ODADF) is crucial for electrical network reliability, the environment, and trading markets. While individual models face challenges in achieving accurate predictions, ensemble learning models have emerged as potential solution. They have achieved success in ODADF in several countries; however, there has been no research conducted for the Irish power system. Therefore, research objectives were formed, to develop a framework of ensemble learning models, evaluate their performance, and examine their potential for ODADF in Ireland, to fill the gap. Experimentation, and CRISP-DM were selected as primary research methodology, and project management framework, respectively. The development of …

Real-Time Polyp Analysis On Endoscopic Images Using Deep Learning Approach: Detection, Characterization And Size Estimation, Phanukorn Sunthornwetchapong

Chulalongkorn University Theses and Dissertations (Chula ETD)

As medical devices advance, doctors adopt endoscopes to perform endoscopes for gastrointestinal disease screenings. For colonoscopy, skills such as polyp detection, polyp characterisation, and polyp size estimation needed to be practised while screening patients. This work aims to develop a deep learning model for performing polyp detection, characterisation, and size estimation to assist fellow doctors in these tasks. To maximise usability in assisting fellow doctors, the model must be able to perform in a real-time fashion. The work utilises existing object detection models for performing size estimation tasks. With the problems of data imbalances, we use depth information but without …

Learned Compressive Representations For Single-Photon 3d Imaging, Felipe Gutierrez-Barragan, Fangzhou Mu, Andrei Ardelean, Atul Ingle, Claudio Bruschini, Edoardo Charbon, Yin Li, Mohit Gupta, Andreas Velten

Computer Science Faculty Publications and Presentations

Single-photon 3D cameras can record the time-of-arrival of billions of photons per second with picosecond accuracy. One common approach to summarize the photon data stream is to build a per-pixel timestamp histogram, resulting in a 3D histogram tensor that encodes distances along the time axis. As the spatio-temporal resolution of the histogram tensor increases, the in-pixel memory requirements and output data rates can quickly become impractical. To overcome this limitation, we propose a family of linear compressive representations of histogram tensors that can be computed efficiently, in an online fashion, as a matrix operation. We design practical lightweight compressive representations …

Leveraging Signal Transfer Characteristics And Parasitics Of Spintronic Circuits For Area And Energy-Optimized Hybrid Digital And Analog Arithmetic, Adrian Tatulian

Electronic Theses and Dissertations, 2020-2023

While Internet of Things (IoT) sensors offer numerous benefits in diverse applications, they are limited by stringent constraints in energy, processing area and memory. These constraints are especially challenging within applications such as Compressive Sensing (CS) and Machine Learning (ML) via Deep Neural Networks (DNNs), which require dot product computations on large data sets. A solution to these challenges has been offered by the development of crossbar array architectures, enabled by recent advances in spintronic devices such as Magnetic Tunnel Junctions (MTJs). Crossbar arrays offer a compact, low-energy and in-memory approach to dot product computation in the analog domain by …

Detection Of Surface Cracks In Metals Using Microwave And Millimeter-Wave Nondestructive Testing Techniques-A Review, Mohamed A. Abou-Khousa, Mohammed Saif Ur Rahman, Kristen M. Donnell, Mohammad Tayeb Al (Tayeb) (Al) Qaseer

Electrical and Computer Engineering Faculty Research & Creative Works

Integrity Assessment of Metallic Structures Requires Inspection Tools Capable of Detecting and Evaluating Cracks Reliably. to This End, Many Microwave and Millimeter-Wave Nondestructive Testing and Evaluation (NDT&E) Methods Have Been Developed and Applied Successfully in the Past. Detection of Fatigue Cracks with Widths Less Than 5 Μ M using Noncontact Microwave-Based Inspection Methods Was Demonstrated in the 1970s. Since their Introduction, These Methods Have Evolved Considerably Toward Enhancing the Detection Sensitivity and Resolution. Undertaking Key Application Challenges Has Attracted Considerable Attention in the Past Three Decades and Led to the Development of the Near-Field Techniques for Crack Detection. to Address …

Analyzing Ground Motion Records With Cvi Fuzzy Art, Dustin Tanksley, Xinzhe Yuan, Genda Chen, Donald C. Wunsch

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

This paper explores using Cluster Validity Indices Fuzzy Adaptative Resonance Theory (CVI Fuzzy ART) to cluster ground motion records (GMRs). Clustering the features extracted from a supervised network trained for predicting the structure damage results in less overfitting from the trained network. Using Cluster Validity Indices (CVIs) to evaluate the clustering gives feedback to how well the data is being classified, allowing further separation of the data. By using CVI Fuzzy ART in combination with features extracted from a trained Convolutional Neural Network (CNN), we were able to form additional clusters in the data. Within the primary clusters, accuracy was …

Optimized Deep Learning Audio Tagging Approach, Fatma S. El-Metwally, Ali I. Eldesouky, Sally M. Elghamrawy

Mansoura Engineering Journal

Audio signal processing is a method for applying powerful algorithms and techniques to record, improve, save and transmit audio content signals. Audio Tagging (AT) is a challenge that requires predicting the tags of audio clips. Developments in deep learning and audio signal processing have resulted in a significant improvement in audio tagging. Many techniques have been used. Several studies have introduced different audio tagging techniques, but the performance of the results obtained from these studies is insufficient. This study proposes an Optimized Deep Learning Audio Tagging (ODLAT] approach to classify and analyze audio tagging. Each input signal is used to …

Parallel Real Time Rrt*: An Rrt* Based Path Planning Process, David Yackzan

Theses and Dissertations--Mechanical and Aerospace Engineering

This thesis presents a new parallelized real-time path planning process. This process is an extension of the Real-Time Rapidly Exploring Random Trees* (RT-RRT*) algorithm developed by Naderi et al in 2015 [1]. The RT-RRT* algorithm was demonstrated on a simulated two-dimensional dynamic environment while finding paths to a varying target state. We demonstrate that the original algorithm is incapable of running at a sufficient rate for control of a 7-degree-of-freedom (7-DoF) robotic arm while maintaining a path planning tree in 7 dimensions. This limitation is due to the complexity of maintaining a tree in a high-dimensional space and the network …

Evaluation Of Different Machine Learning, Deep Learning And Text Processing Techniques For Hate Speech Detection, Nabil Shawkat

Graduate Theses/Dissertations

Social media has become a domain that involves a lot of hate speech. Some users feel entitled to engage in abusive conversations by sending abusive messages, tweets, or photos to other users. It is critical to detect hate speech and prevent innocent users from becoming victims. In this study, I explore the effectiveness and performance of various machine learning methods employing text processing techniques to create a robust system for hate speech identification. I assess the performance of Naïve Bayes, Support Vector Machines, Decision Trees, Random Forests, Logistic Regression, and K Nearest Neighbors using three distinct datasets sourced from social …

Classifying Sidewalk Materials Using Multi-Modal Data, Jiawei Liu

Dissertations and Theses

Navigating safely and independently presents considerable challenges for people who are blind or have low vision (BLV), as it requires a comprehensive understanding of their neighborhood environment. Our user study reveals that materials and objects on sidewalks play a crucial role in navigation tasks. Unfortunately, current methods for assessing sidewalk materials are suboptimal, often relying on labor-intensive and expensive manual assessments that fail to capture the full range of sidewalk features critical to individuals with BLV.

In response to this problem, this master’s thesis investigates deep learning approaches specifically designed for the classification of multi-modal sidewalk materials. The proposed framework …

Demo Alleviate: Demonstrating Artificial Intelligence Enabled Virtual Assistance For Telehealth: The Mental Health Case, Kaushik Roy, Vedant Khandelwal, Raxit Goswami, Nathan Dolbir, Jinendra Malekar, Amit Sheth

Publications

After the pandemic, artificial intelligence (AI) powered support for mental health care has become increasingly important. The breadth and complexity of significant challenges required to provide adequate care involve: (a) Personalized patient understanding, (b) Safety-constrained and medically validated chatbot patient interactions, and (c) Support for continued feedback-based refinements in design using chatbot-patient interactions. We propose Alleviate, a chatbot designed to assist patients suffering from mental health challenges with personalized care and assist clinicians with understanding their patients better. Alleviate draws from an array of publicly available clinically valid mental-health texts and databases, allowing Alleviate to make medically sound and informed …

Alternatives To Reducing Aviation Fuel-Burn With Technology: Fully Electric Autonomous Taxibot, Denzil Neo

Student Works

Aircraft taxiing operations in the aerodrome were identified to consume the most jet fuel apart from the cruise phase of the flight. This was also well supported by various research associating taxi operations at large, congested airports, with high jet fuel consumption, high carbon emissions, and noise pollution. Existing literature recognised the potential to address the environmental issues of aerodrome taxi operations by operating External or Onboard Aircraft Ground Propulsion Systems (AGPS). Designed to power aircraft with sources other than their main engines, external Aircraft Ground Power Systems (AGPS) have shown the potential to significantly cut jet fuel consumption and …

A Survey And Evaluation Of Android-Based Malware Evasion Techniques And Detection Frameworks, Parvez Faruki, Rhati Bhan, Vinesh Jain, Sajal Bhatia, Nour El Madhoun, Rajendra Pamula

School of Computer Science & Engineering Faculty Publications

Android platform security is an active area of research where malware detection techniques continuously evolve to identify novel malware and improve the timely and accurate detection of existing malware. Adversaries are constantly in charge of employing innovative techniques to avoid or prolong malware detection effectively. Past studies have shown that malware detection systems are susceptible to evasion attacks where adversaries can successfully bypass the existing security defenses and deliver the malware to the target system without being detected. The evolution of escape-resistant systems is an open research problem. This paper presents a detailed taxonomy and evaluation of Android-based malware evasion …

A Survey And Evaluation Of Android-Based Malware Evasion Techniques And Detection Frameworks, Parvez Faruki, Rhati Bhan, Vinesh Jain, Sajal Bhatia, Nour El Madhoun, Rajendra Pamula

School of Computer Science & Engineering Faculty Publications

Android platform security is an active area of research where malware detection techniques continuously evolve to identify novel malware and improve the timely and accurate detection of existing malware. Adversaries are constantly in charge of employing innovative techniques to avoid or prolong malware detection effectively. Past studies have shown that malware detection systems are susceptible to evasion attacks where adversaries can successfully bypass the existing security defenses and deliver the malware to the target system without being detected. The evolution of escape-resistant systems is an open research problem. This paper presents a detailed taxonomy and evaluation of Android-based malware evasion …

College Of Computing And Engineering Graduate Catalog 2023-2024, Nova Southeastern University

College of Psychological Services / College of Psychology Postgraduate Student and Course Catalogs

No abstract provided.

Multi-Agent Learning For Game-Theoretical Problems, Kshitija Taywade

Theses and Dissertations--Computer Science

Multi-agent systems are prevalent in the real world in various domains. In many multi-agent systems, interaction among agents is inevitable, and cooperation in some form is needed among agents to deal with the task at hand. We model the type of multi-agent systems where autonomous agents inhabit an environment with no global control or global knowledge, decentralized in the true sense. In particular, we consider game-theoretical problems such as the hedonic coalition formation games, matching problems, and Cournot games. We propose novel decentralized learning and multi-agent reinforcement learning approaches to train agents in learning behaviors and adapting to the environments. …

A Secure And Distributed Architecture For Vehicular Cloud And Protocols For Privacy-Preserving Message Dissemination In Vehicular Ad Hoc Networks, Hassan Mistareehi

Theses and Dissertations--Computer Science

Given the enormous interest in self-driving cars, Vehicular Ad hoc NETworks (VANETs) are likely to be widely deployed in the near future. Cloud computing is also gaining widespread deployment. Marriage between cloud computing and VANETs would help solve many of the needs of drivers, law enforcement agencies, traffic management, etc. The contributions of this dissertation are summarized as follows: A Secure and Distributed Architecture for Vehicular Cloud: Ensuring security and privacy is an important issue in the vehicular cloud; if information exchanged between entities is modified by a malicious vehicle, serious consequences such as traffic congestion and accidents can …

Machine-Learning-Powered Cyber-Physical Systems, Enrico Casella

Theses and Dissertations--Computer Science

In the last few years, we witnessed the revolution of the Internet of Things (IoT) paradigm and the consequent growth of Cyber-Physical Systems (CPSs). IoT devices, which include a plethora of smart interconnected sensors, actuators, and microcontrollers, have the ability to sense physical phenomena occurring in an environment and provide copious amounts of heterogeneous data about the functioning of a system. As a consequence, the large amounts of generated data represent an opportunity to adopt artificial intelligence and machine learning techniques that can be used to make informed decisions aimed at the optimization of such systems, thus enabling a variety …

Hard-Hearted Scrolls: A Noninvasive Method For Reading The Herculaneum Papyri, Stephen Parsons

Theses and Dissertations--Computer Science

The Herculaneum scrolls were buried and carbonized by the eruption of Mount Vesuvius in A.D. 79 and represent the only classical library discovered in situ. Charred by the heat of the eruption, the scrolls are extremely fragile. Since their discovery two centuries ago, some scrolls have been physically opened, leading to some textual recovery but also widespread damage. Many other scrolls remain in rolled form, with unknown contents. More recently, various noninvasive methods have been attempted to reveal the hidden contents of these scrolls using advanced imaging. Unfortunately, their complex internal structure and lack of clear ink contrast has prevented …

Deep Learning-Based Intrusion Detection Methods For Computer Networks And Privacy-Preserving Authentication Method For Vehicular Ad Hoc Networks, Ayesha Dina

Theses and Dissertations--Computer Science

The incidence of computer network intrusions has significantly increased over the last decade, partially attributed to a thriving underground cyber-crime economy and the widespread availability of advanced tools for launching such attacks. To counter these attacks, researchers in both academia and industry have turned to machine learning (ML) techniques to develop Intrusion Detection Systems (IDSes) for computer networks. However, many of the datasets use to train ML classifiers for detecting intrusions are not balanced, with some classes having fewer samples than others. This can result in ML classifiers producing suboptimal results. In this dissertation, we address this issue and present …