Engineering Commons^™

Non-Volatile Memory Adaptation In Asynchronous Microcontroller For Low Leakage Power And Fast Turn-On Time, Jean Pierre Thierry Habimana

Graduate Theses and Dissertations

This dissertation presents an MSP430 microcontroller implementation using Multi-Threshold NULL Convention Logic (MTNCL) methodology combined with an asynchronous non-volatile magnetic random-access-memory (RAM) to achieve low leakage power and fast turn-on. This asynchronous non-volatile RAM is designed with a Spin-Transfer Torque (STT) memory device model and CMOS transistors in a 65 nm technology. A self-timed Quasi-Delay-Insensitive 1 KB STT RAM is designed with an MTNCL interface and handshaking protocol. A replica methodology is implemented to handle write operation completion detection for long state-switching delays of the STT memory device. The MTNCL MSP430 core is integrated with the STT RAM to create …

Low-Power And Reconfigurable Asynchronous Asic Design Implementing Recurrent Neural Networks, Spencer Nelson

Graduate Theses and Dissertations

Artificial intelligence (AI) has experienced a tremendous surge in recent years, resulting in high demand for a wide array of implementations of algorithms in the field. With the rise of Internet-of-Things devices, the need for artificial intelligence algorithms implemented in hardware with tight design restrictions has become even more prevalent. In terms of low power and area, ASIC implementations have the best case. However, these implementations suffer from high non-recurring engineering costs, long time-to-market, and a complete lack of flexibility, which significantly hurts their appeal in an environment where time-to-market is so critical. The time-to-market gap can be shortened through …

Securing Fog Federation From Behavior Of Rogue Nodes, Mohammed Saleh H. Alshehri

Graduate Theses and Dissertations

As the technological revolution advanced information security evolved with an increased need for confidential data protection on the internet. Individuals and organizations typically prefer outsourcing their confidential data to the cloud for processing and storage. As promising as the cloud computing paradigm is, it creates challenges; everything from data security to time latency issues with data computation and delivery to end-users. In response to these challenges CISCO introduced the fog computing paradigm in 2012. The intent was to overcome issues such as time latency and communication overhead and to bring computing and storage resources close to the ground and the …

Multi-Style Explainable Matrix Factorization Techniques For Recommender Systems., Olurotimi Nugbepo Seton

Electronic Theses and Dissertations

Black-box recommender system models are machine learning models that generate personalized recommendations without explaining how the recommendations were generated to the user or giving them a way to correct wrong assumptions made about them by the model. However, compared to white-box models, which are transparent and scrutable, black-box models are generally more accurate. Recent research has shown that accuracy alone is not sufficient for user satisfaction. One such black-box model is Matrix Factorization, a State of the Art recommendation technique that is widely used due to its ability to deal with sparse data sets and to produce accurate recommendations. Recent …

A Framework To Detect The Susceptibility Of Employees To Social Engineering Attacks, Hashim H. Alneami

Doctoral Dissertations and Master's Theses

Social engineering attacks (SE-attacks) in enterprises are hastily growing and are becoming increasingly sophisticated. Generally, SE-attacks involve the psychological manipulation of employees into revealing confidential and valuable company data to cybercriminals. The ramifications could bring devastating financial and irreparable reputation loss to the companies. Because SE-attacks involve a human element, preventing these attacks can be tricky and challenging and has become a topic of interest for many researchers and security experts. While methods exist for detecting SE-attacks, our literature review of existing methods identified many crucial factors such as the national cultural, organizational, and personality traits of employees that enable …

Dynamic Task Allocation In Partially Defined Environments Using A* With Bounded Costs, James Hendrickson

Doctoral Dissertations and Master's Theses

The sector of maritime robotics has seen a boom in operations in areas such as surveying and mapping, clean-up, inspections, search and rescue, law enforcement, and national defense. As this sector has continued to grow, there has been an increased need for single unmanned systems to be able to undertake more complex and greater numbers of tasks. As the maritime domain can be particularly difficult for autonomous vehicles to operate in due to the partially defined nature of the environment, it is crucial that a method exists which is capable of dynamically accomplishing tasks within this operational domain. By considering …

A Hyperelastic Porous Media Framework For Ionic Polymer-Metal Composites And Characterization Of Transduction Phenomena Via Dimensional Analysis And Nonlinear Regression, Zakai J. Olsen

UNLV Theses, Dissertations, Professional Papers, and Capstones

Ionic polymer-metal composites (IPMC) are smart materials that exhibit large deformation in response to small applied voltages, and conversely generate detectable electrical signals in response to mechanical deformations. The study of IPMC materials is a rich field of research, and an interesting intersection of material science, electrochemistry, continuum mechanics, and thermodynamics. Due to their electromechanical and mechanoelectrical transduction capabilities, IPMCs find many applications in robotics, soft robotics, artificial muscles, and biomimetics. This study aims to investigate the dominating physical phenomena that underly the actuation and sensing behavior of IPMC materials. This analysis is made possible by developing a new, hyperelastic …

Synthesizer Parameter Approximation By Deep Learning, Daniel Faronbi, Alisa Gilmore

Theses/Capstones/Creative Projects

Synthesizers have been an essential tool for composers of any style of music including computer generated sound. They allow for an expansion in timbral variety to the orchestration of a piece of music or sound scape. Sound designers are trained to be able to recreate a timbre in their head using a synthesizer. This works well for simple sounds but becomes more difficult as the number of parameters required to produce a specific timbre increase. The goal of this research project is to formulate a method for synthesizers to approximate a timbre given an input audio sample using deep learning. …

A Moving Target Defense For Securing Cyber-Physical Systems, Paul Griffioen, Sean Weerakkody, Bruno Sinopoli

Faculty Work Comprehensive List

This article considers the design and analysis of multiple moving target defenses for recognizing and isolating attacks on cyber-physical systems. We consider attackers who perform integrity attacks on some set of sensors and actuators in a control system. In such cases, it has been shown that a model aware adversary can carefully design attack vectors to bypass bad data detection and identification filters while causing damage to the control system. To counter such an attacker, we propose the moving target defense which introduces stochastic, time-varying parameters in the control system. The underlying random dynamics of the system limit an attacker’s …

Teachability And Interpretability In Reinforcement Learning, Jeevan Rajagopal

School of Computing: Dissertations, Theses, and Student Research

There have been many recent advancements in the field of reinforcement learning, starting from the Deep Q Network playing various Atari 2600 games all the way to Google Deempind's Alphastar playing competitively in the game StarCraft. However, as the field challenges more complex environments, the current methods of training models and understanding their decision making become less effective. Currently, the problem is partially dealt with by simply adding more resources, but the need for a better solution remains.

This thesis proposes a reinforcement learning framework where a teacher or entity with domain knowledge of the task to complete can assist …

“The Revolution Will Not Be Supervised": An Investigation Of The Efficacy And Reasoning Process Of Self-Supervised Representations, Atharva Tendle

School of Computing: Dissertations, Theses, and Student Research

Transfer learning technique enables training Deep Learning (DL) models in a data-efficient way for solving computer vision tasks. It involves pretraining a DL model to learn representations from a large and general-purpose source dataset, then fine-tuning the model using the task-specific target dataset. The dominant supervised learning (SL) approach for pretraining representations suffers from some limitations that include expensive labeling and poor generalizability. Recent advancements in the self-supervised learning (SSL) approach made it possible to learn effective representations from unlabeled data. The performance of the fine-tuned DL models based on pretrained SSL representations is on par with the state-of-the-art pretrained …

Computational Frameworks For Microrna Functional Analysis Of Inter-Kingdom And Indirect Targeting., Mohammed Sayed

Electronic Theses and Dissertations

Genes are DNA sequences that encode the information needed to synthesize molecules necessary for the function of the cell. Some genes are called protein-coding genes because they have the code required to manufacture proteins. The expression of a certain gene means its product (protein) is produced. Although some genes are not protein-coding, they regulate the gene expression of other protein-coding genes. Of these, microRNAs (miRNAs) are small RNA molecules that inhibit the expression of other genes by binding to their mRNA transcripts. miRNAs have been shown to be linked to several biological processes like development and diseases like cancer. Recently, …

Machine Learning Approaches For Lung Cancer Diagnosis., Ahmed Mahmoud Ahmed Shaffie

Electronic Theses and Dissertations

The enormity of changes and development in the field of medical imaging technology is hard to fathom, as it does not just represent the technique and process of constructing visual representations of the body from inside for medical analysis and to reveal the internal structure of different organs under the skin, but also it provides a noninvasive way for diagnosis of various disease and suggest an efficient ways to treat them. While data surrounding all of our lives are stored and collected to be ready for analysis by data scientists, medical images are considered a rich source that could provide …

Restaurant Style Prediction Using Word2vec And Support Vector Machine., Saleh Abdullah Almohaimeed

Electronic Theses and Dissertations

Natural Language Processing represents a quantum leap for governance and industries. It enables them to have an insight into hidden patterns and information within their data. In this thesis, we have worked on an important field in Natural Language Processing, which is Text Classification. Our goal is to help restaurant owners to find which dishes customers like more. To do that we have used a dataset from Yelp.com that has 150,000 restaurant reviews, then count the most frequent dishes mentioned. However, this way is not effective except if these reviews are categorized into different restaurants-styles. For this reason, we have …

A Study Of Deep Reinforcement Learning In Autonomous Racing Using Deepracer Car, Mukesh Ghimire

Honors Theses

Reinforcement learning is thought to be a promising branch of machine learning that has the potential to help us develop an Artificial General Intelligence (AGI) machine. Among the machine learning algorithms, primarily, supervised, semi supervised, unsupervised and reinforcement learning, reinforcement learning is different in a sense that it explores the environment without prior knowledge, and determines the optimal action. This study attempts to understand the concept behind reinforcement learning, the mathematics behind it and see it in action by deploying the trained model in Amazon's DeepRacer car. DeepRacer, a 1/18^th scaled autonomous car, is the agent which is trained …

Automated Bottling Station - Packaging System, Lauren Nicole Zinzilieta

Honors College Theses

This report focuses on the packaging system used at the end of the FESTO bottling station that is located in EP 2355. The system will be made up of an x-y 300 mm x 300 mm gantry system, an Arduino, two micro-stepping stepper drivers, and two 152.4 mm (6 in) single acting cylinders with a 3D printed bracket attached to both. The gantry system will be located at the end of the conveyor belt and will be programmed to pick up three bottles at a time and then will move a specified number of steps in both the x and …

Path Planning With Deep Neural Networks, Paul Simmerling, Brendan Sayers, Paulo Alcantara Silva

Honors Scholar Theses

This report will cover the work and plans of the ECE 2107 Senior design team. The goal of the project is to design and build a fully autonomous self-driving car. This car will have a complete sensor suite including LIDAR, an IMU, a camera, and encoders. It will be based on a multi-level system where the highest level uses a neural network for advanced signal processing and analysis. The current state of the project is discussed as well as the final results. Project management and other constraints will be briefly investigated. This team is building a self driving car testbed …

The Impact Of Artificial Intelligence In The Customer Journey: A Case Study Of Bosch Usa And Defy South Africa, Tariro S. Munyengeterwa

Electronic Theses and Dissertations

Artificial intelligence (AI) continues to gain traction and is increasingly reshaping the media and marketing communications field. While significant research has been conducted on the impact of AI in other fields, there is little empirical evidence on how AI is affecting the customer journey. The present study explored both organizations’ current use of AI tools and how customer perceptions about AI affect AI usage and adoption through the lens of diffusion of innovation theory. The research was conducted using mixed-method qualitative research. In-depth interviews and a case study content analysis were conducted to collect and analyze the data. The results …

Autonomous Aerial Vehicle Vision And Sensor Guided Landing, Gabriel Bitencourt, Elijah J. Brown, Cedric Bleimling, Gilbert Lai, Arman Molki, Tolga Kaya

School of Computer Science & Engineering Faculty Publications

The use of autonomous landing of aerial vehicles is increasing in demand. Applications of this ability can range from simple drone delivery to unmanned military missions. To be able to land at a spot identified by local information, such as a visual marker, creates an efficient and versatile solution. This allows for a more user/consumer friendly device overall. To achieve this goal the use of computer vision and an array of ranging sensors will be explored. In our approach we utilized an April Tag as our location identifier and point of reference. MATLAB/Simulink interface was used to develop the platform …

Ship-Gan: Generative Modeling Based Maritime Traffic Simulator, Chaithanya Shankaramurthy Basrur, Arambam James Singh, Arunesh Sinha, Akshat Kumar

Research Collection School Of Computing and Information Systems

Modeling vessel movement in a maritime environment is an extremely challenging task given the complex nature of vessel behavior. Several existing multiagent maritime decision making frameworks require access to an accurate traffic simulator. We develop a system using electronic navigation charts to generate realistic and high fidelity vessel traffic data using Generative Adversarial Networks (GANs). Our proposed Ship-GAN uses a conditional Wasserstein GAN to model a vessel’s behavior. The generator can simulate the travel time of vessels across different maritime zones conditioned on vessels’ speeds and traffic intensity. Furthermore, it can be used as an accurate simulator for prior decision …

Heterogeneous Graph-Based User-Specific Review Helpfulness Prediction, Dongkai Chen

Dartmouth College Master’s Theses

With the popularity of e-commerce and review websites, it is becoming increasingly important to identify the helpfulness of reviews. However, existing works on predicting reviews’ helpfulness have three major issues: (i) the correlation between helpfulness and features from review text is not clear yet, although many standard features are proposed, (ii) the relations between users, reviews and products have not been considered, (iii) the effectiveness of the existing approaches have not been systematically compared. To address these challenges, we first analyze the correlation between standard features and review helpfulness that are widely used in other work. Based on this analysis, …

A Fully-Automated, Deep Learning-Based Framework For Ct-Based Localization, Segmentation, Verification And Planning Of Metastatic Vertebrae, Tucker Netherton, Tucker James Netherton

Dissertations and Theses (Open Access)

Palliative radiotherapy is an effective treatment for the palliation of symptoms caused by vertebral metastases. Visible evidence of disease is localized on medical images as part of the treatment planning process. However, complicating factors such as time pressures, anatomic variants in the spine, and similarities in adjacent vertebrae are associated with wrong level treatments of the spine. In addition, erroneous manual contouring of anatomic structures is a major failure mode in radiotherapy treatment planning.

The purpose of this study is to mitigate the challenges associated with treatment planning of the spine by automating the treatment planning process for three-dimensional conformal …

Improving Treatment Of Local Liver Ablation Therapy With Deep Learning And Biomechanical Modeling, Brian Anderson, Kristy Brock, Laurence Court, Carlos Eduardo Cardenas, Erik Cressman, Ankit Patel

Dissertations and Theses (Open Access)

In the United States, colorectal cancer is the third most diagnosed cancer, and 60-70% of patients will develop liver metastasis. While surgical liver resection of metastasis is the standard of care for treatment with curative intent, it is only avai lable to about 20% of patients. For patients who are not surgical candidates, local percutaneous ablation therapy (PTA) has been shown to have a similar 5-year overall survival rate. However, PTA can be a challenging procedure, largely due to spatial uncertainties in the localization of the ablation probe, and in measuring the delivered ablation margin.

For this work, we hypothesized …

Wearables And Wearable Data In Tele-Health Applications, Jack Mazza

Honors Theses

With the sudden emergence of Covid-19, Tele-Health has been forced into the forefront of healthcare. With no human contact, regular in-person doctor or clinic visits could not be made. Unfortunately, there is a gap in patient data for healthcare professionals when making diagnoses remotely. Fortunately, many users are constantly collecting some primary health data through wearables that have become commonplace in users' homes. Tapping into this unused data could provide healthcare professionals with a better picture of patients' health remotely. In this thesis, I will determine whether this wearable data can be a viable addition to Tele-Health applications, providing additional …

Demo Abstract: Porting And Execution Of Anomalies Detection Models On Embedded Systems In Iot, Bharath Sudharsan, Pankesh Patel, Abdul Wahid, Muhammad Yahya, John G. Breslin, Muhammad Intizar Ali

Publications

In the Industry 4.0 era, Microcontrollers (MCUs) based tiny embedded sensor systems have become the sensing paradigm to interact with the physical world. In 2020, 25.6 billion MCUs were shipped, and over 250 billion MCUs are already operating in the wild. Such low-power, low-cost MCUs are being used as the brain to control diverse applications and soon will become the global digital nervous system. In an Industrial IoT setup, such tiny MCU-based embedded systems are equipped with anomaly detection models and mounted on production plant machines for monitoring the machine’s health/condition. These models process the machine’s health data (from temperature, …

Supporting Transportation System Management And Operations Using Internet Of Things Technology, Hong Yang, Yuzhong Shen, Mecit Cetin, Zhenyu Wang

Computational Modeling & Simulation Engineering Faculty Publications

Low power wide-area network (LPWAN) technology aims to provide long range and low power wireless communication. It can serve as an alternative technology for data transmissions in many application scenarios (e.g., parking monitoring and remote flood sensing). In order to explore its feasibility in transportation systems, this project conducted a review of relevant literature to understand the current status of LPWAN applications. An online survey that targeted professionals concerned with transportation was also developed to elicit input about their experiences in using LPWAN technology for their projects. The literature review and survey results showed that LPWAN’s application in the U.S. …

Interactive Search Vs. Automatic Search: An Extensive Study On Video Retrieval, Phuong-Anh Nguyen, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

This article conducts user evaluation to study the performance difference between interactive and automatic search. Particularly, the study aims to provide empirical insights of how the performance landscape of video search changes, with tens of thousands of concept detectors freely available to exploit for query formulation. We compare three types of search modes: free-to-play (i.e., search from scratch), non-free-to-play (i.e., search by inspecting results provided by automatic search), and automatic search including concept-free and concept-based retrieval paradigms. The study involves a total of 40 participants; each performs interactive search over 15 queries of various difficulty levels using two search modes …

A New Configuration Of Paralleled Modular Anpc Multilevel Converter Controlled By An Improved Modulation Method For 1 Mhz, 1 Mw Ev Charger, Mostafa Abarzadeh, Waqar A. Khan, Nathan Weise, Kamal Al-Haddad, Ayman M. El-Refaie

Electrical and Computer Engineering Faculty Research and Publications

In this article, a new configuration of the modular multilevel converter (MLC) based on the parallel connection of three-level active-neutral-point-clamped (3L-ANPC) cells as well as its improved modulation method is proposed for 1 MHz, 1 MW electric vehicle (EV) megacharger. In the proposed paralleled modular ANPC-MLC, only six high-frequency silicon carbide (SiC) power switches operating at 333 kHz are required to generate 1 MHz switching frequency spectrum. Moreover, the operating voltage of all power devices is halved, the magnitude of the first switching frequency harmonic cluster is decreased by the factor of five, and the load current is equally distributed …

Reliability And Performance Improvement Of Puc Converter Using A New Single-Carrier Sensor-Less Pwm Method With Pseudo Reference Functions, Mostafa Abarzadeh, Saeed Peyghami, Kamal Al-Haddad, Nathan Weise, Liuchen Chang, Frede Blaabjerg

Electrical and Computer Engineering Faculty Research and Publications

A new single-carrier sensor-less pulsewidth modulation (PWM) method using suggested pseudo reference functions is proposed for packed U-cell (PUC) converter to improve performance and reliability of the PUC converter. It is composed of one PWM carrier signal and two suggested pseudo reference functions. By employing the proposed modulation method, the PUC dc capacitor voltage ripple is substantially decreased, and faster sensor-less capacitor voltage balancing is obtained. Moreover, the power losses are evenly distributed among all power switches. Consequently, notable reduction of the PUC dc capacitor voltage ripple and even distribution of the power loss among switches enhance the PUC converter's …

A Deep Learning-Based Automatic Object Detection Method For Autonomous Driving Ships, Ojonoka Erika Atawodi

Master's Theses

An important feature of an Autonomous Surface Vehicles (ASV) is its capability of automatic object detection to avoid collisions, obstacles and navigate on their own.

Deep learning has made some significant headway in solving fundamental challenges associated with object detection and computer vision. With tremendous demand and advancement in the technologies associated with ASVs, a growing interest in applying deep learning techniques in handling challenges pertaining to autonomous ship driving has substantially increased over the years.

In this thesis, we study, design, and implement an object recognition framework that detects and recognizes objects found in the sea. We first curated …