Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Deep learning (4)
- Image Processing (4)
- Security (3)
- Artificial Intelligence (2)
- Computer Vision (2)
-
- Machine Learning (2)
- Safety (2)
- Software (2)
- Adversarial machine learning (1)
- Agentless (1)
- Algorithm (1)
- Alignment quality assessment (1)
- Analog computing (1)
- Android (1)
- Application (1)
- Authentication (1)
- Big Data (1)
- Bloom filter (1)
- Bowel Sounds (1)
- Building Energy Modeling (1)
- CAN network (1)
- CTIS (1)
- CUDA (1)
- Certainty map (1)
- Chattanooga (1)
- Climate (1)
- Clustering (1)
- Code injection (1)
- Compressed Sensing (1)
- Computational imaging (1)
- Publication Year
Articles 1 - 30 of 41
Full-Text Articles in Entire DC Network
Exploration Of Event-Based Camera Data With Spiking Neural Networks, Charles Peter Rizzo
Exploration Of Event-Based Camera Data With Spiking Neural Networks, Charles Peter Rizzo
Doctoral Dissertations
Neuromorphic computing is a novel, non-von Neumann computing architecture that employs power efficient spiking neural networks on specialized hardware. Taking inspiration from the human brain, spiking neural networks are temporal computation units that propagate information throughout the network via binary spikes. Compared to conventional artificial neural networks, these networks can be more sparse, smaller in size, and more efficient power-wise when realized on neuromorphic hardware. Event-based cameras are novel vision sensors that capture visual information through a temporal stream of events instead of as a conventional RGB frame. These cameras are low-power collections of pixels that asynchronously emit events over …
Deep Learning Based Power System Stability Assessment For Reduced Wecc System, Yinfeng Zhao
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 …
Toward Generating Efficient Deep Neural Networks, Chengcheng Li
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 …
Learning With Limited Labeled Data For Image And Video Understanding, Razieh Kaviani Baghbaderani
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 …
Federated Agentless Detection Of Endpoints Using Behavioral And Characteristic Modeling, Hansaka Angel Dias Edirisinghe Kodituwakku
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 …
Towards Secure Deep Neural Networks For Cyber-Physical Systems, Jiangnan Li
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
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
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 …
A Secure Architecture For Defense Against Return Address Corruption, Grayson J. Bruner
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 …
Analysis Of Hardware Accelerated Deep Learning And The Effects Of Degradation On Performance, Samuel C. Leach
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 …
Efficient Elevator Algorithm, Sean M. Toll, Owen Barbour, Carl Edwards, Daniel Nichols, Austin Day
Efficient Elevator Algorithm, Sean M. Toll, Owen Barbour, Carl Edwards, Daniel Nichols, Austin Day
Chancellor’s Honors Program Projects
No abstract provided.
Modeling The Consumer Acceptance Of Retail Service Robots, So Young Song
Modeling The Consumer Acceptance Of Retail Service Robots, So Young Song
Doctoral Dissertations
This study uses the Computers Are Social Actors (CASA) and domestication theories as the underlying framework of an acceptance model of retail service robots (RSRs). The model illustrates the relationships among facilitators, attitudes toward Human-Robot Interaction (HRI), anxiety toward robots, anticipated service quality, and the acceptance of RSRs. Specifically, the researcher investigates the extent to which the facilitators of usefulness, social capability, the appearance of RSRs, and the attitudes toward HRI affect acceptance and increase the anticipation of service quality. The researcher also tests the inhibiting role of pre-existing anxiety toward robots on the relationship between these facilitators and attitudes …
Computational Imaging Approach To Recovery Of Target Coordinates Using Orbital Sensor Data, Michael D. Vaughan
Computational Imaging Approach To Recovery Of Target Coordinates Using Orbital Sensor Data, Michael D. Vaughan
Doctoral Dissertations
This dissertation addresses the components necessary for simulation of an image-based recovery of the position of a target using orbital image sensors. Each component is considered in detail, focusing on the effect that design choices and system parameters have on the accuracy of the position estimate. Changes in sensor resolution, varying amounts of blur, differences in image noise level, selection of algorithms used for each component, and lag introduced by excessive processing time all contribute to the accuracy of the result regarding recovery of target coordinates using orbital sensor data.
Using physical targets and sensors in this scenario would be …
Wide-Area Measurement-Driven Approaches For Power System Modeling And Analytics, Hesen Liu
Wide-Area Measurement-Driven Approaches For Power System Modeling And Analytics, Hesen Liu
Doctoral Dissertations
This dissertation presents wide-area measurement-driven approaches for power system modeling and analytics. Accurate power system dynamic models are the very basis of power system analysis, control, and operation. Meanwhile, phasor measurement data provide first-hand knowledge of power system dynamic behaviors. The idea of building out innovative applications with synchrophasor data is promising.
Taking advantage of the real-time wide-area measurements, one of phasor measurements’ novel applications is to develop a synchrophasor-based auto-regressive with exogenous inputs (ARX) model that can be updated online to estimate or predict system dynamic responses.
Furthermore, since auto-regressive models are in a big family, the ARX model …
A Probabilistic Software Framework For Scalable Data Storage And Integrity Check, Sisi Xiong
A Probabilistic Software Framework For Scalable Data Storage And Integrity Check, Sisi Xiong
Doctoral Dissertations
Data has overwhelmed the digital world in terms of volume, variety and velocity. Data- intensive applications are facing unprecedented challenges. On the other hand, computation resources, such as memory, suffer from shortage comparing to data scale. However, in certain applications, it is a must to process large amount of data in a time efficient manner. Probabilistic approaches are compromises between these three perspectives: large amount of data, limited computation resources and high time efficiency, in the sense that those approaches cannot guarantee 100% correctness, their error rates, however, are predictable and adjustable depending on available computation resources and time constraints. …
Scheduling For Timely Passenger Delivery In A Large Scale Ride Sharing System, Yang Zhang
Scheduling For Timely Passenger Delivery In A Large Scale Ride Sharing System, Yang Zhang
Masters Theses
Taxi ride sharing is one of the most promising solutions to urban transportation issues, such as traffic congestion, gas insufficiency, air pollution, limited parking space and unaffordable parking charge, taxi shortage in peak hours, etc. Despite the enormous demands of such service and its exciting social benefits, there is still a shortage of successful automated operations of ride sharing systems around the world. Two of the bottlenecks are: (1) on-time delivery is not guaranteed; (2) matching and scheduling drivers and passengers is a NP-hard problem, and optimization based models do not support real time scheduling on large scale systems.
This …
Architecture For Real-Time, Low-Swap Embedded Vision Using Fpgas, Steven Andrew Clukey
Architecture For Real-Time, Low-Swap Embedded Vision Using Fpgas, Steven Andrew Clukey
Masters Theses
In this thesis we designed, prototyped, and constructed a printed circuit board for real-time, low size, weight, and power (SWaP) HDMI video processing and developed a general purpose library of image processing functions for FPGAs.
The printed circuit board is a baseboard for a Xilinx Zynq based system-on-module (SoM). The board provides power, HDMI input, and HDMI output to the SoM and enables low-SWaP, high-resolution, real-time video processing.
The image processing library for FPGAs is designed for high performance and high reusability. These objectives are achieved by utilizing the Chisel hardware construction language to create parameterized modules that construct low-level …
Extending Capability And Implementing A Web Interface For The Xalt Software Monitoring Tool, Kapil Agrawal
Extending Capability And Implementing A Web Interface For The Xalt Software Monitoring Tool, Kapil Agrawal
Masters Theses
As high performance computing centers evolve in terms of hardware, software, and user-base, the act of monitoring and managing such systems requires specialized tools. The tool discussed in this thesis is XALT, which is a collaborative effort between the National Institute for Computational Sciences and Texas Advanced Computing Center. XALT is designed to track link-time and job level information for applications that are compiled and executed on any Linux cluster, workstation, or high-end supercomputer. The key objectives of this work are to extend the existing functionality of XALT and implement a real-time web portal to easily visualize the tracked data. …
Standardizing Functional Safety Assessments For Off-The-Shelf Instrumentation And Controls, Andrew Michael Nack
Standardizing Functional Safety Assessments For Off-The-Shelf Instrumentation And Controls, Andrew Michael Nack
Masters Theses
It is typical for digital instrumentation and controls, used to manage significant risk, to undergo substantial amounts of scrutiny. The equipment must be proven to have the necessary level of design integrity. The details of the scrutiny vary based on the particular industry, but the ultimate goal is to provide sufficient evidence that the equipment will operate successfully when performing their required functions.
To be able to stand up to the scrutiny and more importantly, successfully perform the required safety functions, the equipment must be designed to defend against random hardware failures and also to prevent systematic faults. These design …
Hyperspectral Data Acquisition And Its Application For Face Recognition, Woon Cho
Hyperspectral Data Acquisition And Its Application For Face Recognition, Woon Cho
Doctoral Dissertations
Current face recognition systems are rife with serious challenges in uncontrolled conditions: e.g., unrestrained lighting, pose variations, accessories, etc. Hyperspectral imaging (HI) is typically employed to counter many of those challenges, by incorporating the spectral information within different bands. Although numerous methods based on hyperspectral imaging have been developed for face recognition with promising results, three fundamental challenges remain: 1) low signal to noise ratios and low intensity values in the bands of the hyperspectral image specifically near blue bands; 2) high dimensionality of hyperspectral data; and 3) inter-band misalignment (IBM) correlated with subject motion during data acquisition.
This dissertation …
Compressed Sensing In Resource-Constrained Environments: From Sensing Mechanism Design To Recovery Algorithms, Shuangjiang Li
Compressed Sensing In Resource-Constrained Environments: From Sensing Mechanism Design To Recovery Algorithms, Shuangjiang Li
Doctoral Dissertations
Compressed Sensing (CS) is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for reconstruction. It is promising that CS can be utilized in environments where the signal acquisition process is extremely difficult or costly, e.g., a resource-constrained environment like the smartphone platform, or a band-limited environment like visual sensor network (VSNs). There are several challenges to perform sensing due to the characteristic of these platforms, including, for example, needing active user involvement, computational and storage limitations and lower transmission capabilities. This dissertation focuses on the study of …
Scalable Hardware Efficient Deep Spatio-Temporal Inference Networks, Steven Robert Young
Scalable Hardware Efficient Deep Spatio-Temporal Inference Networks, Steven Robert Young
Doctoral Dissertations
Deep machine learning (DML) is a promising field of research that has enjoyed much success in recent years. Two of the predominant deep learning architectures studied in the literature are Convolutional Neural Networks (CNNs) and Deep Belief Networks (DBNs). Both have been successfully applied to many standard benchmarks with a primary focus on machine vision and speech processing domains.
Many real-world applications involve time-varying signals and, consequently, necessitate models that efficiently represent both temporal and spatial attributes. However, neither DBNs nor CNNs are designed to naturally capture temporal dependencies in observed data, often resulting in the inadequate transformation of spatio-temporal …
Statistical Analysis Of Disturbances In Power Transmission Systems, Liu Liu
Statistical Analysis Of Disturbances In Power Transmission Systems, Liu Liu
Masters Theses
Disturbance analysis is essential to the study of the power transmission systems. Traditionally, disturbances are described as megawatt (MW) events, but the access to data is inefficient due to the slow installation and authorization process of the monitoring device. In this paper, we propose a novel approach to disturbance analysis conducted at the distribution level by exploiting the frequency recordings from Frequency Disturbance Recorders (FDRs) of the Frequency Monitoring Network (FNET/GridEye), based on the relationship between frequency change and the power loss of disturbances - linearly associated by the Frequency Response. We first analyze the real disturbance records of North …
Feature Extraction And Recognition For Human Action Recognition, Jiajia Luo
Feature Extraction And Recognition For Human Action Recognition, Jiajia Luo
Doctoral Dissertations
How to automatically label videos containing human motions is the task of human action recognition. Traditional human action recognition algorithms use the RGB videos as input, and it is a challenging task because of the large intra-class variations of actions, cluttered background, possible camera movement, and illumination variations. Recently, the introduction of cost-effective depth cameras provides a new possibility to address difficult issues. However, it also brings new challenges such as noisy depth maps and time alignment. In this dissertation, effective and computationally efficient feature extraction and recognition algorithms are proposed for human action recognition.
At the feature extraction step, …
Improved Forensic Medical Device Security Through Eating Detection, Nathan Lee Henry
Improved Forensic Medical Device Security Through Eating Detection, Nathan Lee Henry
Masters Theses
Patients are increasingly reliant on implantable medical device systems today. For patients with diabetes, an implantable insulin pump system or artificial pancreas can greatly improve quality of life. As with any device, these devices can and do suffer from software and hardware issues, often reported as a safety event. For a forensic investigator, a safety event is indistinguishable from a potential security event. In this thesis, we show a new sensor system that can be transparently integrated into existing and future electronic diabetes therapy systems while providing additional forensic data to help distinguish between safety and security events. We demonstrate …
Ecocar2 Center Stack Development, Westley Logan Harris, Chris Winstead, Nicholas Alexander Cavopol, William Willie Wells, Tate Glick Hawkersmith
Ecocar2 Center Stack Development, Westley Logan Harris, Chris Winstead, Nicholas Alexander Cavopol, William Willie Wells, Tate Glick Hawkersmith
Chancellor’s Honors Program Projects
No abstract provided.
Online Multi-Stage Deep Architectures For Feature Extraction And Object Recognition, Derek Christopher Rose
Online Multi-Stage Deep Architectures For Feature Extraction And Object Recognition, Derek Christopher Rose
Doctoral Dissertations
Multi-stage visual architectures have recently found success in achieving high classification accuracies over image datasets with large variations in pose, lighting, and scale. Inspired by techniques currently at the forefront of deep learning, such architectures are typically composed of one or more layers of preprocessing, feature encoding, and pooling to extract features from raw images. Training these components traditionally relies on large sets of patches that are extracted from a potentially large image dataset. In this context, high-dimensional feature space representations are often helpful for obtaining the best classification performances and providing a higher degree of invariance to object transformations. …
Automated Generation Of Simulink Models For Enumeration Hybrid Automata, David Aaron Heise
Automated Generation Of Simulink Models For Enumeration Hybrid Automata, David Aaron Heise
Masters Theses
An enumeration hybrid automaton has been shown in principle to be ready for automated transformation into a Simulink implementation. This paper describes a strategy for and a demonstration of automated construction. This is accomplished by designing a data model which represents EHA data and providing a mapping from EHA data points to Simulink blocks.
An Expert System For Guitar Sheet Music To Guitar Tablature, Chuanjun He
An Expert System For Guitar Sheet Music To Guitar Tablature, Chuanjun He
Doctoral Dissertations
This project applies analysis, design and implementation of the Optical Music Recognition (OMR) to an expert system for transforming guitar sheet music to guitar tablature. The first part includes image processing and music semantic interpretation to interpret and transform sheet music or printed scores into editable and playable electronic form. Then after importing the electronic form of music into internal data structures, our application uses effective pruning to explore the entire search space to find the best guitar tablature. Also considered are alternate guitar tunings and transposition of the music to improve the resulting tablature.
Electronic Medical Record Ipad Application, Mischa Symmone Buckler, Dwayne Wiliam Flaherty, John Thomas Cotham, Mark Bellott
Electronic Medical Record Ipad Application, Mischa Symmone Buckler, Dwayne Wiliam Flaherty, John Thomas Cotham, Mark Bellott
Chancellor’s Honors Program Projects
No abstract provided.