Computer Engineering Commons^™

Source Code Plagiarism Detection Using Jplag & Stack Overflow Data, Sudheer Yetthapu

Masters Theses & Specialist Projects

Advancements in computer technology and internet services have led to the availability of vast amounts of information like videos, articles, research papers, and code samples. Free online information will increase the possibility of plagiarism and collusion among students. People can commit plagiarism in both text and code [1], as tools used to detect plagiarism between texts and between codes are distinct. Traditionally plagiarism in code is detected using manual inspection, which is a tedious process and misses to compare code from previous submissions and external sources. To overcome this issue, systems that can automatically detect plagiarism in code were developed …

Green Hpc: Optimizing Software Stack Energy Efficiency Of Large Data Systems, Grant Wilkins

Honors College Theses

High-performance computing (HPC) is indispensable in modern scientific research and industry applications, but its energy consumption is a growing concern. This thesis presents two novel approaches to optimize energy consumption in large data systems. The first chapter of the thesis will discuss the use of Dynamic Voltage and Frequency Scaling (DVFS) to optimize the energy efficiency of two popular lossy compression algorithms: SZ and ZFP. By adjusting the voltage and frequency levels of computing resources, DVFS can reduce energy consumption while maintaining the desired level of performance and accuracy. The second chapter of the thesis will focus on a detailed …

Modeling And Control Strategies For A Two-Wheel Balancing Mobile Robot, John Alan Moritz

Graduate Theses and Dissertations

The problem of balancing and autonomously navigating a two-wheel mobile robot is an increasingly active area of research, due to its potential applications in last-mile delivery, pedestrian transportation, warehouse automation, parts supply, agriculture, surveillance, and monitoring. This thesis investigates the design and control of a two-wheel balancing mobile robot using three different control strategies: Proportional Integral Derivative (PID) controllers, Sliding Mode Control, and Deep Q-Learning methodology. The mobile robot is modeled using a dynamic and kinematic model, and its motion is simulated in a custom MATLAB/Simulink environment. The first part of the thesis focuses on developing a dynamic and kinematic …

Uibee: An Improved Deep Instance Segmentation And Classification Of Ui Elements In Wireframes, Cahi̇t Berkay Kazangi̇rler, Caner Özcan, Buse Yaren Teki̇n

Turkish Journal of Electrical Engineering and Computer Sciences

User Interface (UI) is a basic concept in which individuals interact with any computer program or technological device to create a graphical design. In the initial stages of app development, UI prototype is a must. An automatic analysis system for the basic execution of UI designs will considerably speed up the development of designs according to old-fashioned methods. In this approach, it is aimed at saving cost and time by automating the process. For the aforesaid objective, we present a new approach rather than the traditional methods. For this reason, a high amount of elements in wireframes are detected and …

Adversarial Deep Learning And Security With A Hardware Perspective, Joseph Clements

All Dissertations

Adversarial deep learning is the field of study which analyzes deep learning in the presence of adversarial entities. This entails understanding the capabilities, objectives, and attack scenarios available to the adversary to develop defensive mechanisms and avenues of robustness available to the benign parties. Understanding this facet of deep learning helps us improve the safety of the deep learning systems against external threats from adversaries. However, of equal importance, this perspective also helps the industry understand and respond to critical failures in the technology. The expectation of future success has driven significant interest in developing this technology broadly. Adversarial deep …

Digital Simulations Of Memristors Towards Integration With Reconfigurable Computing, Ivris Raymond

Computer Science and Computer Engineering Undergraduate Honors Theses

The end of Moore’s Law has been predicted for decades. Demand for increased parallel computational performance has been increased by improvements in machine learning. This past decade has demonstrated the ever-increasing creativity and effort necessary to extract scaling improvements in CMOS fabrication processes. However, CMOS scaling is nearing its fundamental physical limits. A viable path for increasing performance is to break the von Neumann bottleneck. In-memory computing using emerging memory technologies (e.g. ReRam, STT, MRAM) offers a potential path beyond the end of Moore’s Law. However, there is currently very little support from industry tools for designers wishing to incorporate …

Beyond Just Money Transactions: Redesigning Digital Peer-To-Peer Payments For Social Connections, Lingyuan Li

All Dissertations

Financial activities, such as the exchange of money between individuals, have long been considered a crucial aspect of how people build and maintain their interpersonal relationships (i.e., a strong, deep, or close association/acquaintance between two or more people) with individuals they know because money is a sensitive social construct. In particular, over the past decade, how to conduct, manage, and experience money exchanges and processes between individuals has been dramatically transformed due to the increasing popularity of digital peer-to-peer (P2P) payment services (i.e., performing one to one online money transactions via a digital device). In this sense, digital P2P payments …

Deep Reinforcement Learning And Game Theoretic Monte Carlo Decision Process For Safe And Efficient Lane Change Maneuver And Speed Management, Shahab Karimi

All Dissertations

Predicting the states of the surrounding traffic is one of the major problems in automated driving. Maneuvers such as lane change, merge, and exit management could pose challenges in the absence of intervehicular communication and can benefit from driver behavior prediction. Predicting the motion of surrounding vehicles and trajectory planning need to be computationally efficient for real-time implementation. This dissertation presents a decision process model for real-time automated lane change and speed management in highway and urban traffic. In lane change and merge maneuvers, it is important to know how neighboring vehicles will act in the imminent future. Human driver …

Vanet Applications Under Loss Scenarios & Evolving Wireless Technology, Adil Alsuhaim

All Dissertations

In this work we study the impact of wireless network impairment on the performance of VANET applications such as Cooperative Adaptive Cruise Control (CACC), and other VANET applications that periodically broadcast messages. We also study the future of VANET application in light of the evolution of radio access technologies (RAT) that are used to exchange messages. Previous work in the literature proposed fallback strategies that utilizes on-board sensors to recover in case of wireless network impairment, those methods assume a fixed time headway value, and do not achieve string stability. In this work, we study the string stability of a …

Enhanced Mobile Networking Using Multi-Connectivity And Packet Duplication In Next-Generation Cellular Networks, Prabodh Mishra

All Dissertations

Modern cellular communication systems need to handle an enormous number of users and large amounts of data, including both users as well as system-oriented data. 5G is the fifth-generation mobile network and a new global wireless standard that follows 4G/LTE networks. The uptake of 5G is expected to be faster than any previous cellular generation, with high expectations of its future impact on the global economy. The next-generation 5G networks are designed to be flexible enough to adapt to modern use cases and be highly modular such that operators would have the flexibility to provide selective features based on user …

Task Offloading And Resource Allocation Based On Dl-Ga In Mobile Edge Computing, Hang Gu, Minjuan Zhang, Wenzao Li, Yuwen Pan

Turkish Journal of Electrical Engineering and Computer Sciences

With the rapid development of 5G and the Internet of Things (IoT), the traditional cloud computing architecture struggle to support the booming computation-intensive and latency-sensitive applications. Mobile edge computing (MEC) has emerged as a solution which enables abundant IoT tasks to be offloaded to edge services. However, task offloading and resource allocation remain challenges in MEC framework. In this paper, we add the total number of offloaded tasks to the optimization objective and apply algorithm called Deep Learning Trained by Genetic Algorithm (DL-GA) to maximize the value function, which is defined as a weighted sum of energy consumption, latency, and …

Breath Analysis For Detection Of Lung Cancer With Hybrid Sensor-Based Electronic Nose, Ümi̇t Özsandikcioğlu, Ayten Atasoy

Turkish Journal of Electrical Engineering and Computer Sciences

Lung cancer has the highest death rates among all types of cancer worldwide. Detection of lung cancer in its early stages significantly increases the survival rate. In this study, the aim is to improve the lung cancer detection performance of electronic noses (e-noses) with breath analysis by using two different types of gas sensor-based e-nose. The developed e-nose system consists of 14 quartz crystal microbalance (QCM) sensors and 8 metal oxide semiconductor (MOS) sensors. Breath samples were collected from a total of 100 volunteers, including 60 patients with lung cancer, 20 healthy nonsmokers, and 20 healthy smokers, and were classified …

Integration Of Neural Network And Distance Relay To Improve The Fault Localization On Transmission Lines, Linh Tran

Turkish Journal of Electrical Engineering and Computer Sciences

Power transmission lines are integral and very important components of power systems. Because of the length of these lines and the complexity of the power grids, the lines may encounter various incidents such as lightning strike, shortage, and breakage. When an incident or a fault occurs, a fast process of identification, localization, and isolation of the fault is desired. An accurate fault localization would have a great impact in reducing the restoration time of the system. One of the most popular solutions for fault detection and localization is the distance relays using the impedance-based algorithms. However, these relays are still …

Quadratic Programming Based Partitioning For Block Cimmino With Correct Value Representation, Zuhal Taş, Fahreddi̇n Şükrü Torun

Turkish Journal of Electrical Engineering and Computer Sciences

The block Cimmino method is successfully used for the parallel solution of large linear systems of equations due to its amenability to parallel processing. Since the convergence rate of block Cimmino depends on the orthogonality between the row blocks, advanced partitioning methods are used for faster convergence. In this work, we propose a new partitioning method that is superior to the state-of-the-art partitioning method, GRIP, in several ways. Firstly, our proposed method exploits the Mongoose partitioning library which can outperform the state-of-the-art methods by combining the advantages of classical combinatoric methods and continuous quadratic programming formulations. Secondly, the proposed method …

Unbiased Federated Learning In Energy Harvesting Error-Prone Channels, Zeynep Çakir, Eli̇f Tuğçe Ceran Arslan

Turkish Journal of Electrical Engineering and Computer Sciences

Federated learning (FL) is a communication-efficient and privacy-preserving learning technique for collaborative training of machine learning models on vast amounts of data produced and stored locally on the distributed users. This paper investigates unbiased FL methods that achieve a similar convergence as state-of-the-art methods in scenarios with various constraints like an error-prone channel or intermittent energy availability. For this purpose, we propose FL algorithms that jointly design unbiased user scheduling and gradient weighting according to each user's distinct energy and channel profile. In addition, we exploit a prevalent metric called the age of information (AoI), which quantifies the staleness of …

Efficient Modelling Of Random Access Memory Cell: An Approach Using Qca Nanocomputing, Ali Newaz Bahar, Angshuman Khan

Turkish Journal of Electrical Engineering and Computer Sciences

Quantum-dot cellular automata (QCA) is innovative and potentially fruitful nanotechnology that provides a solution for transistor-based circuits with enhanced switching frequency, large-scale integration, and low power consumption. The random-access memory (RAM) cell is a fundamental component that is designed to operate quickly and effectively since memory is a core part of the semiconductor industry, thus the QCA family. The RAM cell design in this work is based on a multiplexer structure and is implemented without using coplanar crossovers of QCA technology. QCADesigner-2.0.3, a standard QCA layout design and verification tool, is used in the simulation and validation processes for the …

An Analytical Solution Of Fractional Order Pi Controller Design For Stable/Unstable/Integrating Processes With Time Delay, Erdal Çökmez, İbrahi̇m Kaya

Turkish Journal of Electrical Engineering and Computer Sciences

This paper aims to put forward an analytical solution for tuning parameters of a fractional order PI (FOPI) controller for stable, unstable, and integrating processes with time delay. Following this purpose, the analytical weighted geometrical center (AWGC) method has been extended to the design of fractional order PI controllers. To apply AWGC, the stability equations of the closed-loop system are written in terms of process and fractional order PI controller parameters. With the proposed method, the centroid can be calculated analytically, and the controller parameters can be easily calculated without the need of repetitive drawings of the stability boundary regions. …

More Wifi For Everyone: Increasing Spectral Efficiency In Wifi6 Networks Using A Distributed Obss/Pd Mechanism, Ali̇ Karakoç, Hüseyi̇n Bi̇rkan Yilmaz, Mehmet Şükrü Kuran

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, we propose a distributed algorithm that determines effective Overlapping Basic Service Set/Preamble Detection (OBSS/PD) threshold levels in each WiFi6 device to maximize the total throughput by increasing the spectral efficiency. Within WiFi6 standard, OBSS/PD mechanism is introduced to increase the overall efficiency of WiFi networks by tuning the receiver sensitivity as well as the transmission power. In a nutshell, the proposed algorithm, RACEBOT, tunes the hearing (i.e. reception) and speaking (i.e. transmission) parameters of each WiFi device individually for the betterment of the WiFi experience of all WiFi networks in a neighborhood. WiFi experience is not only …

Sim-To-Real Reinforcement Learning Framework For Autonomous Aerial Leaf Sampling, Ashraful Islam

School of Computing: Dissertations, Theses, and Student Research

Using unmanned aerial systems (UAS) for leaf sampling is contributing to a better understanding of the influence of climate change on plant species, and the dynamics of forest ecology by studying hard-to-reach tree canopies. Currently, multiple skilled operators are required for UAS maneuvering and using the leaf sampling tool. This often limits sampling to only the canopy top or periphery. Sim-to-real reinforcement learning (RL) can be leveraged to tackle challenges in the autonomous operation of aerial leaf sampling in the changing environment of a tree canopy. However, trans- ferring an RL controller that is learned in simulation to real UAS …

Analysis And Implementation Of A New High-Buck Dc-Dc Converter With Interleaved Output Inductors And Soft Switching Capability, Sajad Ghabeli Sani, Mohamad Reza Banaei, Seyed Hossein Hosseini

Turkish Journal of Electrical Engineering and Computer Sciences

This paper proposes an innovative structure for DC-DC converters with high buck gain by using a lower number of elements. The converter provides highly efficient output power and an extended output voltage range. In addition, the distribution of output current between two inductors and the soft-switching capability of the power switches have made the converter suitable for applications that require high output current. All power switches accomplish the ZVZCS (zero-voltage and zero-current switching) condition with the aid of a small auxiliary inductor (Lx), which charges and discharges parallel capacitors of main switches to provide soft-switching conditions. Thus, the switching losses …

An Efficient Deep Learning Architecture For Turkish Lira Recognition And Counterfeit Detection, Burak İyi̇kesi̇ci̇, Ergun Erçelebi̇

Turkish Journal of Electrical Engineering and Computer Sciences

Banknote counterfeiting is a common practice worldwide. Due to the recent developments in technology, banknote imitation has become easier than before. There are different kinds of algorithms developed for the detection of counterfeit banknotes for different countries in the literature. The earlier algorithms utilized classical image processing techniques where the implementations of machine learning and deep learning algorithms appeared with the developments in the artificial intelligence field as well as the computer hardware. In this study, a novel convolutional neural networks-based deep learning algorithm has been developed that detects counterfeit Turkish Lira banknotes and their denominations using the banknote images …

Wearable Sensor Gait Analysis For Fall Detection Using Deep Learning Methods, Haben Girmay Yhdego

Electrical & Computer Engineering Theses & Dissertations

World Health Organization (WHO) data show that around 684,000 people die from falls yearly, making it the second-highest mortality rate after traffic accidents [1]. Early detection of falls, followed by pneumatic protection, is one of the most effective means of ensuring the safety of the elderly. In light of the recent widespread adoption of wearable sensors, it has become increasingly critical that fall detection models are developed that can effectively process large and sequential sensor signal data. Several researchers have recently developed fall detection algorithms based on wearable sensor data. However, real-time fall detection remains challenging because of the wide …

A Long-Term Funds Predictor Based On Deep Learning, Shuiyi Kuang

Electronic Theses, Projects, and Dissertations

Numerous neural network models have been created to predict the rise or fall of stocks since deep learning has gained popularity, and many of them have performed quite well. However, since the share market is hugely influenced by various policy changes or unexpected news, it is challenging for investors to use such short-term predictions as a guide. In this paper, we try to find a suitable long-term predictor for the funds market by testing different kinds of neural network models, including the Long Short-Term Memory(LSTM) model with different layers, the Gated Recurrent Units(GRU) model with different layers, and the combination …

Government Aid Portal, Darshan Togadiya

Electronic Theses, Projects, and Dissertations

In today’s world, contacting government officials seems a big task when it comes to reporting small concerns. There are many authorities and officials which makes it very difficult for ordinary people to figure out who they should contact to resolve their daily issues. To address this problem, I have developed an application which can act as intermediary between citizens and government authorities. This portal will enable locals to submit complaints regarding personal or general issues through a complaint form, which will then be routed to the appropriate government department. Once a complaint is filed, government teams are immediately alerted and …

Intelligent And Explainable Solution To Predict Infant Birthweight And Preterm Birth In The United Arab Emirates, Wasif Khan

Dissertations

Adverse pregnancy outcomes such as Low Birth Weight (LBW) and Preterm birth (PTB) are complex pregnancy challenges that can lead to high perinatal mortality and long-term morbidity for infants. Early prediction of such adverse outcomes can be useful for averting catastrophic outcomes for the mother and her baby. With advances in machine learning (ML)-based algorithms, several models have been proposed for both PTB and LBW predictions. However, the risk factors associated with these outcomes are still unknown, particularly in the United Arab Emirates (UAE). Furthermore, existing ML-based prediction models work in a black-box manner and lack proper interpretations for clinicians, …

Information-Theoretic Model Diagnostics (Infomod), Armin Esmaeilzadeh

UNLV Theses, Dissertations, Professional Papers, and Capstones

Model validation is a critical step in the development, deployment, and governance of machine learning models. During the validation process, the predictive power of a model is measured on unseen datasets with a variety of metrics such as Accuracy and F1-Scores for classification tasks. Although the most used metrics are easy to implement and understand, they are aggregate measures over all the segments of heterogeneous datasets, and therefore, they do not identify the performance variation of a model among different data segments. The lack of insight into how the model performs over segments of unseen datasets has raised significant challenges …

Protecting The Infrastructure Of Michigan: Analyzing And Understanding Internet Infrastructure, Samuel Blaser, Travis Munyer, Damian Ramirez, Lester Juarez, Jackson Servant

Theses/Capstones/Creative Projects

The Michigan Army National Guard DCOE is hoping to increase their understanding of the physical, electrical, protocol, and logical topography of internet service. In order to understand the infrastructure of the internet, information must be collected on its pieces. By studying, describing, and illustrating the infrastructure of the global internet we can develop hardening tactics, improve user training, and develop contingency plans in the case of an attack. The research is focused on where data lives, locating data centers in the region, identifying global infrastructure and who owns it, and potential for hardening. An interactive map has been created in …

Radic Voice Authentication: Replay Attack Detection Using Image Classification For Voice Authentication Systems, Hannah Taylor

Undergraduate Honors Theses

Systems like Google Home, Alexa, and Siri that use voice-based authentication to verify their users’ identities are vulnerable to voice replay attacks. These attacks gain unauthorized access to voice-controlled devices or systems by replaying recordings of passphrases and voice commands. This shows the necessity to develop more resilient voice-based authentication systems that can detect voice replay attacks.

This thesis implements a system that detects voice-based replay attacks by using deep learning and image classification of voice spectrograms to differentiate between live and recorded speech. Tests of this system indicate that the approach represents a promising direction for detecting voice-based replay …

The Rapid Increase Of Ransomware Attacks Over The 21st Century And Mitigation Strategies To Prevent Them From Arising, Sanjay Jacob

Senior Honors Theses

Cyber-attacks have continued to become more common throughout the past century as more people are exposed to the Internet. Every year, various studies, reports, and scholarly research is done to emphasis the rapid increase of attacks. In this honors thesis, the student sought to gather further information about the rise of ransomware attacks, various cyber threats, discuss the psychological manipulation that exist, and provided the reader with an ethical complement of cyber-attacks. Additionally, case studies from previous research have been analyzed and mitigation strategies have been explained to provide the reader with practical application. This research emphasizes in on key …

Cross-Platform Development Of Wake-Up-Word, Christopher Ryan Woodle

Theses and Dissertations

The goal of this project will be to explore cross-platform implementation of Wake-Up- Word (WUW). To enable the development of future speech-based artificial intelligence applications, it is important to have robust and accessible implementations of WUW. Adoption of Unix based operating systems continues to expand for server, backend, and embedded applications, therefore a WUW implementation in Unix will become essential. As web technologies continue to grow, WUW will also need to be implemented in web, using technologies such as JavaScript and Web Assembly (WASM). This project encompasses porting the previous implementation of WUW from Microsoft Windows to Unix, building a …