Engineering Commons^™

Quantitative Metrics For Mutation Testing, Amani M. Ayad

Dissertations

Program mutation is the process of generating versions of a base program by applying elementary syntactic modifications; this technique has been used in program testing in a variety of applications, most notably to assess the quality of a test data set. A good test set will discover the difference between the original program and mutant except if the mutant is semantically equivalent to the original program, despite being syntactically distinct.

Equivalent mutants are a major nuisance in the practice of mutation testing, because they introduce a significant amount of bias and uncertainty in the analysis of test results; indeed, mutants …

Design And Implementation Of Anomaly Detections For User Authentication Framework, Iman Abu Sulayman

Electronic Thesis and Dissertation Repository

Anomaly detection is quickly becoming a very significant tool for a variety of applications such as intrusion detection, fraud detection, fault detection, system health monitoring, and event detection in IoT devices. An application that lacks a strong implementation for anomaly detection is user trait modeling for user authentication purposes. User trait models expose up-to-date representation of the user so that changes in their interests, their learning progress or interactions with the system are noticed and interpreted. The reason behind the lack of adoption in user trait modeling arises from the need of a continuous flow of high-volume data, that is …

Analysis Of The Duration And Energy Consumption Of Aes Algorithms On A Contiki-Based Iot Device, Brandon Tsao

Computer Science and Engineering Master's Theses

With the growing prevalence of the Internet of Things, securing the sheer abundance of devices is critical. The current IoT and security landscapes lack empirical metrics on encryption algorithm implementations that are optimized for constrained devices, such as encryption/decryption duration and energy consumption. In this paper, we achieve two things. First, we survey for optimized implementations of symmetric encryption algorithms. Seconds, we study the performance of various symmetric encryption algorithms on a Contiki-based IoT device. This paper provides encryption and decryption durations and energy consumption results on three implementations of AES: TinyAES, B-Con’s AES, and Contiki’s own built-in AES. In …

Non-Trivial Off-Path Network Measurements Without Shared Side-Channel Resource Exhaustion, Geoffrey I. Alexander

Computer Science ETDs

Most traditional network measurement scans and attacks are carried out through the use of direct, on-path network packet transmission. This requires that a machine be on-path (i.e, involved in the packet transmission process) and as a result have direct access to the data packets being transmitted. This limits network scans and attacks to situations where access can be gained to an on-path machine. If, for example, a researcher wanted to measure the round trip time between two machines they did not have access to, traditional scans would be of little help as they require access to an on-path machine to …

Leveraging Cloud-Based Nfv And Sdn Platform Towards Quality-Driven Next-Generation Mobile Networks, Hassan Hawilo

Electronic Thesis and Dissertation Repository

Network virtualization has become a key approach for Network Service Providers (NSPs) to mitigate the challenge of the continually increasing demands for network services. Tightly coupled with their software components, legacy network devices are difficult to upgrade or modify to meet the dynamically changing end-user needs. To virtualize their infrastructure and mitigate those challenges, NSPs have started to adopt Software Defined Networking (SDN) and Network Function Virtualization (NFV). To this end, this thesis addresses the challenges faced on the road of transforming the legacy networking infrastructure to a more dynamic and agile virtualized environment to meet the rapidly increasing demand …

Cluster-Based Chained Transfer Learning For Energy Forecasting With Big Data, Yifang Tian

Electronic Thesis and Dissertation Repository

Smart meter popularity has resulted in the ability to collect big energy data and has created opportunities for large-scale energy forecasting. Machine Learning (ML) techniques commonly used for forecasting, such as neural networks, involve computationally intensive training typically with data from a single building/group to predict future consumption for that same building/group. With hundreds of thousands of smart meters, it becomes impractical or even infeasible to individually train a model for each meter. Consequently, this paper proposes Cluster-Based Chained Transfer Learning (CBCTL), an approach for building neural network-based models for many meters by taking advantage of already trained models through …

Extreme Image Compression With Deep Learning Autoencoder, Licheng Xiao

Computer Science and Engineering Master's Theses

Image compression can save billions of dollars in the industry by reducing the bits needed to store and transfer an image without significantly losing visual quality. Traditional image compression methods use transform, quantization, predictive coding and entropy coding to tackle the problem, represented by international standards like JPEG (joint photographic experts group), JPEG 2000, BPG (better portable graphics), and HEIC (high efficiency image file format). Recently, there are deep learning based image compression approaches that achieved similar or better performance compared with traditional methods, represented by autoencoder, GAN (generative adversarial networks) and super-resolution based approaches.

In this paper, we built …

Robot Motion Planning In Dynamic Environments, Hao-Tien Lewis Chiang

Computer Science ETDs

Robot motion planning in dynamic environments is critical for many robotic applications, such as self-driving cars, UAVs and service robots operating in changing environments. However, motion planning in dynamic environments is very challenging as this problem has been shown to be NP-Hard and in PSPACE, even in the simplest case. As a result, the lack of safe, efficient planning solutions for real-world robots is one of the biggest obstacles for ubiquitous adoption of robots in everyday life. Specifically, there are four main challenges facing motion planning in dynamic environments: obstacle motion uncertainty, obstacle interaction, complex robot dynamics and noise, and …

A Wearable Mechatronic Device For Hand Tremor Monitoring And Suppression: Development And Evaluation, Yue Zhou

Electronic Thesis and Dissertation Repository

Tremor, one of the most disabling symptoms of Parkinson's disease (PD), significantly affects the quality of life of the individuals who suffer from it. These people live with difficulties with fine motor tasks, such as eating and writing, and suffer from social embarrassment. Traditional medicines are often ineffective, and surgery is highly invasive and risky. The emergence of wearable technology facilitates an externally worn mechatronic tremor suppression device as a potential alternative approach for tremor management. However, no device has been developed for the suppression of finger tremor that has been validated on a human.

It has been reported in …

The Fog Development Kit: A Platform For The Development And Management Of Fog Systems, Colton Powell

Computer Science and Engineering Master's Theses

With the rise of the Internet of Things (IoT), fog computing has emerged to help traditional cloud computing in meeting scalability demands. Fog computing makes it possible to fulfill real-time requirements of applications by bringing more processing, storage, and control power geographically closer to end-devices. How- ever, since fog computing is a relatively new field, there is no standard platform for research and development in a realistic environment, and this dramatically inhibits innovation and development of fog-based applications. In response to these challenges, we propose the Fog Development Kit (FDK). By providing high-level interfaces for allocating computing and networking resources, …

Home Automation System By Voice Commands, Noor Kamil Abdalhameed

Theses and Dissertations

The Home Automation System is one of the most important technologies that are used by humans for controlling electrical devices to reduce manual efforts in their daily tasks. The home automation system by voice has the ability to understand thousands of voice commands and perform the required action to control various electrical devices. The voice recognition is a bit complex and challenging task since each person has his accent. Therefore, Bitvoicer Server used in the home automation system in this thesis since it supports 17 languages from 26 countries and regions, and has the ability to recognize an unlimited number …

Reasoning From Point Clouds, Joey Wilson

Computer Engineering

Over the past two years, 3D object detection has been a major area of focus across industry and academia. This is primarily due to the difficulty of learning data from point clouds. While camera images are fixed size and can therefore be easily trained on using convolution, point clouds are unstructured series of points in three dimensions. Therefore, there is no fixed number of features, or a structure to run convolution on. Instead, researchers have developed many ways of attempting to learn from this data, however there is no clear consensus on what is the best method, as each has …

Android And Web Application For Tracking Employees, Kaival Dholakia

Electronic Theses, Projects, and Dissertations

The purpose that this tracking system serves is to keep track of the employees of the company who have the nature of their job which involves a lot of traveling to various locations on a day to day basis. It is an amalgamation of Android as well as a Web application. The employee is supposed to pass the location and image as per the terms and conditions specified to use the Android application. The web application is used by the admin department to access the information which would help them monitor the location of the employee in a timely manner. …

Secure Two-Party Protocol For Privacy-Preserving Classification Via Differential Privacy, Manish Kumar

Boise State University Theses and Dissertations

Privacy-preserving distributed data mining is the study of mining on distributed data—owned by multiple data owners—in a non-secure environment, where the mining protocol does not reveal any sensitive information to the data owners, the individual privacy is preserved, and the output mining model is practically useful. In this thesis, we propose a secure two-party protocol for building a privacy-preserving decision tree classifier over distributed data using differential privacy. We utilize secure multiparty computation to ensure that the protocol is privacy-preserving. Our algorithm also utilizes parallel and sequential compositions, and applies distributed exponential mechanism to ensure that the output is differentially-private. …

Extending The Capabilities Of Von Neumann With A Dataflow Sub-Isa, Martin Cowley

Masters Theses

Instruction set architectures (ISAs) such as x86, ARM, and RISC-V follow the control flow model of computation, where a program is defined as a sequence of instructions. Early processors executed instructions one-by-one based on the control flow of a program. Dataflow is an alternative model of computation that uses the availability of data to drive instruction execution. Any instruction can be chosen for execution, independent of the instruction order, as long as the data is available for that instruction. While modern processors incorporate concepts of the dataflow model in the microarchitecture, the implementation of the ISA, the amount of instruction …

Mitigating Pilot Contamination Through Optimizing Pilot Allocation In Massive Mimo Systems, Rand Abdul Hussain

Theses and Dissertations

This dissertation has proposed several algorithms to optimize the allocation of pilots to the users’ equipment (UEs) to mitigate the effect of the pilot contamination problem in the massive MIMO systems. Pilot contamination reduces the performance of massive MIMO systems due to the reduction in the quality of the estimated channel between a UE and the serving base station (BS). The limitation of the number of samples in a coherence block limits the number of unique mutually orthogonal pilots, and hence, reusing the set of pilots across the cells causes inter-cell interference during pilot transmission, which is called pilot contamination. …

Socialization Of Veterans Using Virtual Reality, Joan M. Savage

Theses and Dissertations

Virtual reality, augmented reality, mixed reality, and video games are growing in popularity and fulfilling genuine human needs that the real world is currently unable to satisfy. Games are providing rewards that reality is not. They are teaching and inspiring and engaging us in ways that reality is not (McGonigal, 2011). The purpose of this study was to capture the essence of socialization in virtual reality as a Ph.D. dissertation topic at Florida Institute of Technology - Human-Centered Design. This study used a phenomenology methodology to capture the experiences of beneficial features of players who use virtual reality. The study …

Machine Learning In The State Design Pattern, Timothy Matthew Von Friesen

Theses and Dissertations

As the Internet of Things revolution continues to become more prevalent in humanity's daily routine, securing these devices is paramount. Society has seen a substantial increase in activity in the cyber-warfare battle space, resulting in an increasing amount of security breaches every year. The responsibility of securing our devices can no longer rely solely on cyber-security engineers keeping systems hardened through Security Technical Implementation Guides and vulnerability scans; it must shift towards the developer. Previous research has been done in this area of securing our devices. However, these solutions rely heavily on cloud computing resources to perform computationally expensive algorithms. …

Resource Allocation And Task Scheduling Optimization In Cloud-Based Content Delivery Networks With Edge Computing, Yang Peng

Operations Research and Engineering Management Theses and Dissertations

The extensive growth in adoption of mobile devices pushes global Internet protocol (IP) traffic to grow and content delivery network (CDN) will carry 72 percent of total Internet traffic by 2022, up from 56 percent in 2017. In this praxis, Interconnected Cache Edge (ICE) based on different public cloud infrastructures with multiple edge computing sites is considered to help CDN service providers (SPs) to maximize their operational profit. The problem of resource allocation and performance optimization is studied in order to maximize the cache hit ratio with available CDN capacity.

The considered problem is formulated as a multi-stage stochastic linear …

Multiple Face Detection And Recognition System Design Applying Deep Learning In Web Browsers Using Javascript, Cristhian Gabriel Espinosa Sandoval

Computer Science and Computer Engineering Undergraduate Honors Theses

Deep learning has advanced progressively in the last years and now demonstrates state-of-the-art performance in various fields. In the era of big data, transformation of data into valuable knowledge has become one of the most important challenges in computing. Therefore, we will review multiple algorithms for face recognition that have been researched for a long time and are maturely developed, and analyze deep learning, presenting examples of current research.

To provide a useful and comprehensive perspective, in this paper we categorize research by deep learning architecture, including neural networks, convolutional neural networks, depthwise Separable Convolutions, densely connected convolutional networks, and …

Image-Driven Automated End-To-End Testing For Mobile Applications, Caleb Fritz

Computer Science and Computer Engineering Undergraduate Honors Theses

The increasing complexity and demand of software systems and the greater availability of test automation software is quickly rendering manual end-to-end (E2E) testing techniques for mobile platforms obsolete. This research seeks to explore the potential increase in automated test efficacy and maintainability through the use of computer vision algorithms when applied with Appium, a leading cross-platform mobile test automation framework. A testing framework written in a Node.js environment was created to support the development of E2E test scripts that examine and report the functional capabilities of a mobile test app. The test framework provides a suite of functions that connect …

Involuntary Signal-Based Grounding Of Civilian Unmanned Aerial Systems (Uas) In Civilian Airspace, Keith Conley

Master's Theses

This thesis investigates the involuntary signal-based grounding of civilian unmanned aerial systems (UAS) in unauthorized air spaces. The technique proposed here will forcibly land unauthorized UAS in a given area in such a way that the UAS will not be harmed, and the pilot cannot stop the landing. The technique will not involuntarily ground authorized drones which will be determined prior to the landing. Unauthorized airspaces include military bases, university campuses, areas affected by a natural disaster, and stadiums for public events. This thesis proposes an early prototype of a hardware-based signal based involuntary grounding technique to handle the problem …

Extracting Social Network From Literary Prose, Tarana Tasmin Bipasha

Graduate Theses and Dissertations

This thesis develops an approach to extract social networks from literary prose, namely, Jane Austen’s published novels from eighteenth- and nineteenth- century. Dialogue interaction plays a key role while we derive the networks, thus our technique relies upon our ability to determine when two characters are in conversation. Our process involves encoding plain literary text into the Text Encoding Initiative’s (TEI) XML format, character name identification, conversation and co-occurrence detection, and social network construction. Previous work in social network construction for literature have focused on drama, specifically manually TEI-encoded Shakespearean plays in which character interactions are much easier to track …

Evaluation And Analysis Of Null Convention Logic Circuits, John Davis Brady

Graduate Theses and Dissertations

Integrated circuit (IC) designers face many challenges in utilizing state-of-the-art technology nodes, such as the increased effects of process variation on timing analysis and heterogeneous multi-die architectures that span across multiple technologies while simultaneously increasing performance and decreasing power consumption. These challenges provide opportunity for utilization of asynchronous design paradigms due to their inherent flexibility and robustness.

While NULL Convention Logic (NCL) has been implemented in a variety of applications, current literature does not fully encompass the intricacies of NCL power performance across a variety of applications, technology nodes, circuit scale, and voltage scaling, thereby preventing further adoption and utilization …

Design And Development Of A Comprehensive And Interactive Diabetic Parameter Monitoring System - Betictrack, Nusrat Chowdhury

Electronic Theses and Dissertations

A novel, interactive Android app has been developed that monitors the health of type 2 diabetic patients in real-time, providing patients and their physicians with real-time feedback on all relevant parameters of diabetes. The app includes modules for recording carbohydrate intake and blood glucose; for reminding patients about the need to take medications on schedule; and for tracking physical activity, using movement data via Bluetooth from a pair of wearable insole devices. Two machine learning models were developed to detect seven physical activities: sitting, standing, walking, running, stair ascent, stair descent and use of elliptical trainers. The SVM and decision …

Improved Study Of Side-Channel Attacks Using Recurrent Neural Networks, Muhammad Abu Naser Rony Chowdhury

Boise State University Theses and Dissertations

Differential power analysis attacks are special kinds of side-channel attacks where power traces are considered as the side-channel information to launch the attack. These attacks are threatening and significant security issues for modern cryptographic devices such as smart cards, and Point of Sale (POS) machine; because after careful analysis of the power traces, the attacker can break any secured encryption algorithm and can steal sensitive information.

In our work, we study differential power analysis attack using two popular neural networks: Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN). Our work seeks to answer three research questions(RQs):

RQ1: Is it …

Introducing Digital Content To Kclc, Chad Briesacher

Theses

No abstract provided.

Rfid Item-Level Tagging In A Grocery Store Environment, Brian Truman

LSU Master's Theses

The purpose of this research was to investigate how effective item-level Radio Frequency Identification (RFID) tagging would be using current RFID technology as a replacement for barcodes in a supermarket/grocery store environment.

To accomplish this, an experiment was be performed that utilized commercially available RFID technology. Passive Ultra High Frequency (UHF) RFID Tags were affixed to various grocery store items of different material categories (Food, Metal, Plastic, Liquid, and Glass), and placed in a metal shopping cart. Eight (8) antenna arrangements were created, comprised of different combinations of four (4) antennas in different locations around the cart.

The experiment was …

Effective Fuzzing Framework For The Sleuthkit Tools, Shravya Paruchuri

LSU Master's Theses

The fields of digital forensics and incident response have seen significant growth over the last decade due to the increasing threats faced by organizations and the continued reliance on digital platforms and devices by criminals. In the past, digital investigations were performed manually by expert investigators, but this approach has become no longer viable given the amount of data that must be processed compared to the relatively small number of trained investigators. These resource constraints have led to the development and reliance on automated processing and analysis systems for digital evidence. In this paper, we present our effort to develop …

A Parallel Direct Method For Finite Element Electromagnetic Computations Based On Domain Decomposition, Javad Moshfegh

Doctoral Dissertations

High performance parallel computing and direct (factorization-based) solution methods have been the two main trends in electromagnetic computations in recent years. When time-harmonic (frequency-domain) Maxwell's equation are directly discretized with the Finite Element Method (FEM) or other Partial Differential Equation (PDE) methods, the resulting linear system of equations is sparse and indefinite, thus harder to efficiently factorize serially or in parallel than alternative methods e.g. integral equation solutions, that result in dense linear systems. State-of-the-art sparse matrix direct solvers such as MUMPS and PARDISO don't scale favorably, have low parallel efficiency and high memory footprint. This work introduces a new …