Computer Engineering Commons^™

Developing A Flexible System For A Friendly Robot To Ease Dementia (Fred) Using Cloud Technologies And Software Design Patterns, Robert James Bray

Masters Theses

In this work, we designed two prototypes for a friendly robot to ease dementia (FRED). This affordable social robot is designed to provide company to older adults with cognitive decline, create reminders for important events and tasks, like taking medication, and providing cognitive stimulus through games. This project combines several cloud technologies including speech-to-text, cloud data storage, and chat generation in order to provide high level interactions with a social robot. Software design patterns were employed in the creation of the software to produce flexible code base that can sustain platform changes easily, including the framework used for the graphical …

Anomaly Detection On Complex Health Information Technology Systems, Haoran Niu

Doctoral Dissertations

While modern complex computer systems provide enormous benefits to our daily lives, the increasing complexity of these large-scale systems also makes them more susceptible to unexpected software malfunctions and malicious attacks. This is especially true for Health Information Technology (HIT), which has revolutionized healthcare delivery by making it more efficient, effective, and accessible. Nevertheless, the widespread adoption of HIT has introduced new challenges related to ensuring system reliability and security. As a result, the development of novel algorithms and frameworks to detect anomalies in such systems has become increasingly important for enhancing patient safety and improving the efficiency and effectiveness …

Deep Learning Based Power System Stability Assessment For Reduced Wecc System, Yinfeng Zhao

Doctoral Dissertations

Power system stability is the ability of power system, for a giving initial operating condition, to reach a new operation condition with most of the system variables bounded in normal range after subjecting to a short or long disturbance. Traditional power system stability mainly uses time-domain simulation which is very time consuming and only appropriate for offline assessment.

Nowadays, with increasing penetration of inverter based renewable, large-scale distributed energy storage integration and operation uncertainty brought by weather and electricity market, system dynamic and operating condition is more dramatic, and traditional power system stability assessment based on scheduling may not be …

Development Of A Soft Robotic Approach For An Intra-Abdominal Wireless Laparoscopic Camera, Hui Liu

Doctoral Dissertations

In Single-Incision Laparoscopic Surgery (SILS), the Magnetic Anchoring and Guidance System (MAGS) arises as a promising technique to provide larger workspaces and field of vision for the laparoscopes, relief space for other instruments, and require fewer incisions. Inspired by MAGS, many concept designs related to fully insertable magnetically driven laparoscopes are developed and tested on the transabdominal operation. However, ignoring the tissue interaction and insertion procedure, most of the designs adopt rigid structures, which not only damage the patients' tissue with excess stress concentration and sliding motion but also require complicated operation for the insertion. Meanwhile, lacking state tracking of …

Toward Generating Efficient Deep Neural Networks, Chengcheng Li

Doctoral Dissertations

Recent advances in deep neural networks have led to tremendous applications in various tasks, such as object classification and detection, image synthesis, natural language processing, game playing, and biological imaging. However, deploying these pre-trained networks on resource-limited devices poses a challenge, as most state-of- the-art networks contain millions of parameters, making them cumbersome and slow in real-world applications. To address this problem, numerous network compression and acceleration approaches, also known as efficient deep neural networks or efficient deep learning, have been investigated, in terms of hardware and software (algorithms), training, and inference. The aim of this dissertation is to study …

Benchmarking Of Embedded Object Detection In Optical And Radar Scenes, Vijaysrinivas Rajagopal

Masters Theses

A portable, real-time vital sign estimation protoype is developed using neural network- based localization, multi-object tracking, and embedded processing optimizations. The system estimates heart and respiration rates of multiple subjects using directional of arrival techniques on RADAR data. This system is useful in many civilian and military applications including search and rescue.

The primary contribution from this work is the implementation and benchmarking of neural networks for real time detection and localization on various systems including the testing of eight neural networks on a discrete GPU and Jetson Xavier devices. Mean average precision (mAP) and inference speed benchmarks were performed. …

Learning With Limited Labeled Data For Image And Video Understanding, Razieh Kaviani Baghbaderani

Doctoral Dissertations

Deep learning-based algorithms have remarkably improved the performance in many computer vision tasks. However, deep networks often demand a large-scale and carefully annotated dataset and sufficient sample coverage of every training category. However, it is not practical in many real-world applications where only a few examples may be available, or the data annotation is costly and require expert knowledge. To mitigate this issue, learning with limited data has gained considerable attention and is investigated thorough different learning methods, including few-shot learning, weakly/semi supervised learning, open-set learning, etc.

In this work, the classification problem is investigated under an open-world assumption to …

Task-Oriented Manipulation Planning: Teaching Robot Manipulators To Learn Trajectory Tasks, Yan Li

Doctoral Dissertations

As robot manipulator applications are conducted in more complex tasks and unstructured environments, traditional manual programming cannot match the growing requirements. However, human experts usually know how to operate robot manipulators to complete tasks, but they do not know how to manually program the robot for automatically executing tasks. From a general point of view, a robot manipulation task is composed of a series of consecutive robot actions and environment states which we call it trajectory task. Imitation learning, an emerging and popular technique of robot behavior programming, is a good way to tackle this line of work but still …

Analysis Of Multiple Adversarial Attacks On Convolutional Neural Networks, Burcum Eken

Masters Theses

The thesis studies different kind of adversarial attacks on Convolutional Neural Network by using electric load data set in order to fool deep neural network. With the improvement of Deep Learning methods, their securities and vulnerabilities have become an important research subject. An adversary who gains access to the model and data sets may add some perturbations to the datasets, which may cause significant damage to the system. By using adversarial attacks, it shows how much these attacks affect the system and shows the attacks' success in this research.

Model Based Force Estimation And Stiffness Control For Continuum Robots, Vincent A. Aloi

Doctoral Dissertations

Continuum Robots are bio-inspired structures that mimic the motion of snakes, elephant trunks, octopus tentacles, etc. With good design, these robots can be naturally compliant and miniaturizable, which makes Continuum Robots ideal for traversing narrow complex environments. Their flexible design, however, prevents us from using traditional methods for controlling and estimating loading on rigid link robots.

In the first thrust of this research, we provided a novel stiffness control law that alters the behavior of an end effector during contact. This controller is applicable to any continuum robot where a method for sensing or estimating tip forces and pose exists. …

Unconventional Computation Including Quantum Computation, Bruce J. Maclennan

Faculty Publications and Other Works -- EECS

Unconventional computation (or non-standard computation) refers to the use of non-traditional technologies and computing paradigms. As we approach the limits of Moore’s Law, progress in computation will depend on going beyond binary electronics and on exploring new paradigms and technologies for information processing and control. This book surveys some topics relevant to unconventional computation, including the definition of unconventional computations, the physics of computation, quantum computation, DNA and molecular computation, and analog computation. This book is the content of a course taught at UTK.

Federated Agentless Detection Of Endpoints Using Behavioral And Characteristic Modeling, Hansaka Angel Dias Edirisinghe Kodituwakku

Doctoral Dissertations

During the past two decades computer networks and security have evolved that, even though we use the same TCP/IP stack, network traffic behaviors and security needs have significantly changed. To secure modern computer networks, complete and accurate data must be gathered in a structured manner pertaining to the network and endpoint behavior. Security operations teams struggle to keep up with the ever-increasing number of devices and network attacks daily. Often the security aspect of networks gets managed reactively instead of providing proactive protection. Data collected at the backbone are becoming inadequate during security incidents. Incident response teams require data that …

Profile-Guided Data Management For Heterogeneous Memory Systems, Matthew B. Olson

Doctoral Dissertations

Market forces and technological constraints have led to a gap between CPU and memory performance that has widened for decades. While processor scaling has plateaued in recent years, this gap persists and is not expected to diminish for the foreseeable future. This discrepancy presents a host of challenges for scaling application performance, which have only been exacerbated in recent years, as increasing demands for fast and effective data analytics are driving memory energy, bandwidth, and capacity requirements to new heights.

To address these trends, hardware architects have introduced a plethora of memory technologies. For example, most modern memory systems include …

Qualitative And Quantitative Improvements For Positron Emission Tomography Using Different Motion Correction Methodologies, Tasmia Rahman Tumpa

Doctoral Dissertations

Positron Emission Tomography (PET) data suffers from low image quality and quantitative accuracy due to different kinds of motion of patients during imaging. Hardware-based motion correction is currently the standard; however, is limited by several constraints, the most important of which is retroactive data correction. Data-driven techniques to perform motion correction in this regard are active areas of research. The motivation behind this work lies in developing a complete data-driven approach to address both motion detection and correction. The work first presents an algorithm based on the positron emission particle tracking (PEPT) technique and makes use of time-of-flight (TOF) information …

Evaluation Of Robust Deep Learning Pipelines Targeting Low Swap Edge Deployment, David Carter Cornett

Masters Theses

The deep learning technique of convolutional neural networks (CNNs) has greatly advanced the state-of-the-art for computer vision tasks such as image classification and object detection. These solutions rely on large systems leveraging wattage-hungry GPUs to provide the computational power to achieve such performance. However, the size, weight and power (SWaP) requirements of these conventional GPU-based deep learning systems are not suitable when a solution requires deployment to so called "Edge" environments such as autonomous vehicles, unmanned aerial vehicles (UAVs) and smart security cameras.

The objective of this work is to benchmark FPGA-based alternatives to conventional GPU systems that have the …

Sabr: Development Of A Neuromorphic Balancing Robot, Alec Yen, Yaw Mensah, Mark Dean

EURēCA: Exhibition of Undergraduate Research and Creative Achievement

We discuss the development of a self-adjusted balancing robot (SABR) using a neuromorphic computing framework for control. Implementations of two-wheeled balancing robots have been achieved using traditional algorithms, often in the form of proportional-integral-derivative (PID) control. We aim to achieve the same task using a neuromorphic architecture, which offers potential for higher power efficiency than conventional processing techniques. We utilize evolutionary optimization (EO) and the second iteration of Dynamic Adaptive Neural Network Arrays (DANNA2) developed by the Laboratory of Tennesseans Exploring Neural Networks (TENNLab). For the purpose of comparison, a traditional balancing robot was first designed using PID control; the …

Sensor Fusion For Object Detection And Tracking In Autonomous Vehicles, Mohamad Ramin Nabati

Doctoral Dissertations

Autonomous driving vehicles depend on their perception system to understand the environment and identify all static and dynamic obstacles surrounding the vehicle. The perception system in an autonomous vehicle uses the sensory data obtained from different sensor modalities to understand the environment and perform a variety of tasks such as object detection and object tracking. Combining the outputs of different sensors to obtain a more reliable and robust outcome is called sensor fusion. This dissertation studies the problem of sensor fusion for object detection and object tracking in autonomous driving vehicles and explores different approaches for utilizing deep neural networks …

Toward Reliable And Efficient Message Passing Software For Hpc Systems: Fault Tolerance And Vector Extension, Dong Zhong

Doctoral Dissertations

As the scale of High-performance Computing (HPC) systems continues to grow, researchers are devoted themselves to achieve the best performance of running long computing jobs on these systems. My research focus on reliability and efficiency study for HPC software.

First, as systems become larger, mean-time-to-failure (MTTF) of these HPC systems is negatively impacted and tends to decrease. Handling system failures becomes a prime challenge. My research aims to present a general design and implementation of an efficient runtime-level failure detection and propagation strategy targeting large-scale, dynamic systems that is able to detect both node and process failures. Using multiple overlapping …

Design And Simulation Of A Supervisory Control System For Hybrid Manufacturing, Michael Buckley

Masters Theses

The research teams of Dr. Bill Hamel, Dr. Bradley Jared and Dr. Tony Schmitz were tasked by the Office of Naval Research to create a hybrid manufacturing process for a reduced scale model of a naval ship propeller. The base structure of the propeller is created using Wire Arc Additive Manufacturing (WAAM), which is then scanned to compare created geometry to desired geometry. The propeller is then machined down to match the desired geometry. This process is iterated upon until the final product meets design tolerances. Due to the complex nature and numerous industrial machines used in the process, it …

Connecting Islamic Technology And The History Of Robotics In Wikidata Via Wikidatabot, Anchalee Panigabutra-Roberts

UT Libraries Faculty: Other Publications and Presentations

My current study is on the connection between the history of robotics and Islamic technology. I focused on early Muslim inventors, such as al-Jazari, from Artuqid Dynasty of Jazira in Mesopotamia (modern day Iraq, Syria and Turkey) who is considered to be the father of robotics. He wrote the Book of Knowledge of Ingenious Mechanical Devices, the manuscript treaty published after his passing in 1206, translated by Donald R. Hill, a British engineer and scholar on Islamic technology, in 1974. The manuscript in Arabic (MS. Greaves 27) is archived at the Bodleian Library, University of Oxford, United Kingdom. In …

Identification Of Emergent Collaborative Behaviors In Multi-Agent Systems, Bryson Howell

EURēCA: Exhibition of Undergraduate Research and Creative Achievement

Identification of Emergent Collaborative Behaviors in Multi-Agent Systems

Bryson Howell

Multi-Agent Reinforcement Learning (MARL) has been used to allow groups of autonomous agents to perform complex cooperative tasks. When MARL methods such as the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm [1] are used to train teams of agents in cooperative tasks, it has been observed that the actions of individual agents are significantly influenced by the actions of their teammates [2]. Additionally, prior work has shown that teams of agents trained independently of one another under identical conditions display a variety of behaviors [3]. Since these teams have been …

Human Fatigue Predictions In Complex Aviation Crew Operational Impact Conditions, Suresh Rangan

Doctoral Dissertations

In this last decade, several regulatory frameworks across the world in all modes of transportation had brought fatigue and its risk management in operations to the forefront. Of all transportation modes air travel has been the safest means of transportation. Still as part of continuous improvement efforts, regulators are insisting the operators to adopt strong fatigue science and its foundational principles to reinforce safety risk assessment and management. Fatigue risk management is a data driven system that finds a realistic balance between safety and productivity in an organization. This work discusses the effects of mathematical modeling of fatigue and its …

Towards Secure Deep Neural Networks For Cyber-Physical Systems, Jiangnan Li

Doctoral Dissertations

In recent years, deep neural networks (DNNs) are increasingly investigated in the literature to be employed in cyber-physical systems (CPSs). DNNs own inherent advantages in complex pattern identifying and achieve state-of-the-art performances in many important CPS applications. However, DNN-based systems usually require large datasets for model training, which introduces new data management issues. Meanwhile, research in the computer vision domain demonstrated that the DNNs are highly vulnerable to adversarial examples. Therefore, the security risks of employing DNNs in CPSs applications are of concern.

In this dissertation, we study the security of employing DNNs in CPSs from both the data domain …

Utility Scale Building Energy Modeling And Climate Impacts, Brett C. Bass

Doctoral Dissertations

Energy consumption is steadily increasing year over year in the United States (US). Climate change and anthropogenically forced shifts in weather have a significant impact on energy use as well as the resilience of the built environment and the electric grid. With buildings accounting for about 40% of total energy use in the US, building energy modeling (BEM) at a large scale is critical. This work advances that effort in a number of ways. First, current BEM approaches, their ability to scale to large geographical areas, and global climate models are reviewed. Next, a methodology for large-scale BEM is illustrated, …

An Analysis Of Modern Password Manager Security And Usage On Desktop And Mobile Devices, Timothy Oesch

Doctoral Dissertations

Security experts recommend password managers to help users generate, store, and enter strong, unique passwords. Prior research confirms that managers do help users move towards these objectives, but it also identified usability and security issues that had the potential to leak user data or prevent users from making full use of their manager. In this dissertation, I set out to measure to what extent modern managers have addressed these security issues on both desktop and mobile environments. Additionally, I have interviewed individuals to understand their password management behavior.

I begin my analysis by conducting the first security evaluation of the …

Analysis Of Hardware Accelerated Deep Learning And The Effects Of Degradation On Performance, Samuel C. Leach

Masters Theses

As convolutional neural networks become more prevalent in research and real world applications, the need for them to be faster and more robust will be a constant battle. This thesis investigates the effect of degradation being introduced to an image prior to object recognition with a convolutional neural network. As well as experimenting with methods to reduce the degradation and improve performance. Gaussian smoothing and additive Gaussian noise are both analyzed degradation models within this thesis and are reduced with Gaussian and Butterworth masks using unsharp masking and smoothing, respectively. The results show that each degradation is disruptive to the …

A Secure Architecture For Defense Against Return Address Corruption, Grayson J. Bruner

Masters Theses

The advent of the Internet of Things has brought about a staggering level of inter-connectivity between common devices used every day. Unfortunately, security is not a high priority for developers designing these IoT devices. Often times the trade-off of security comes at too high of a cost in other areas, such as performance or power consumption. This is especially prevalent in resource-constrained devices, which make up a large number of IoT devices. However, a lack of security could lead to a cascade of security breaches rippling through connected devices. One of the most common attacks used by hackers is return …

Robot Object Detection And Locomotion Demonstration For Eecs Department Tours, Bryson Howell, Ethan Haworth, Chris Mobley, Ian Mulet

Chancellor’s Honors Program Projects

No abstract provided.

Multi-Layer Utilization Of Beamforming In Millimeter Wave Mimo Systems, Mustafa S. Aljumaily

Doctoral Dissertations

mmWave frequencies ranging between (30-300GHz) have been considered the perfect solution to the scarcity of bandwidth in the traditional sub-6GHz band and to the ever increasing demand of many emerging applications in today's era. 5G and beyond standards are all considering the mmWave as an essential part of there networks. Beamforming is one of the most important enabling technologies for the mmWave to compensate for the huge propagation lose of these frequencies compared to the sub-6GHz frequencies and to ensure better spatial and spectral utilization of the mmWave channel space. In this work, we tried to develop different techniques to …

Leveraging Conventional Internet Routing Protocol Behavior To Defeat Ddos And Adverse Networking Conditions, Jared M. Smith

Doctoral Dissertations

The Internet is a cornerstone of modern society. Yet increasingly devastating attacks against the Internet threaten to undermine the Internet's success at connecting the unconnected. Of all the adversarial campaigns waged against the Internet and the organizations that rely on it, distributed denial of service, or DDoS, tops the list of the most volatile attacks. In recent years, DDoS attacks have been responsible for large swaths of the Internet blacking out, while other attacks have completely overwhelmed key Internet services and websites. Core to the Internet's functionality is the way in which traffic on the Internet gets from one destination …