Defense Notices
All students and faculty are welcome to attend the final defense of EECS graduate students completing their M.S. or Ph.D. degrees. Defense notices for M.S./Ph.D. presentations for this year and several previous years are listed below in reverse chronological order.
Students who are nearing the completion of their M.S./Ph.D. research should schedule their final defenses through the EECS graduate office at least THREE WEEKS PRIOR to their presentation date so that there is time to complete the degree requirements check, and post the presentation announcement online.
Upcoming Defense Notices
Mahmudul Hasan
Assertion-Based Security Assessment of Hardware IP Protection MethodsWhen & Where:
Eaton Hall, Room 2001B
Committee Members:
Tamzidul Hoque, ChairEsam El-Araby
Sumaiya Shomaji
Abstract
Combinational and sequential locking methods are promising solutions for protecting hardware intellectual property (IP) from piracy, reverse engineering, and malicious modifications by locking the functionality of the IP based on a secret key. To improve their security, researchers are developing attack methods to extract the secret key.
While the attacks on combinational locking are mostly inapplicable for sequential designs without access to the scan chain, the limited applicable attacks are generally evaluated against the basic random insertion of key gates. On the other hand, attacks on sequential locking techniques suffer from scalability issues and evaluation of improperly locked designs. Finally, while most attacks provide an approximately correct key, they do not indicate which specific key bits are undetermined. This thesis proposes an oracle-guided attack that applies to both combinational and sequential locking without scan chain access. The attack applies light-weight design modifications that represent the oracle using a finite state machine and applies an assertion-based query of the unlocking key. We have analyzed the effectiveness of our attack against 46 sequential designs locked with various classes of combinational locking including random, strong, logic cone-based, and anti-SAT based. We further evaluated against a sequential locking technique using 46 designs with various key sequence lengths and widths. Finally, we expand our framework to identify undetermined key bits, enabling complementary attacks on the smaller remaining key space.
Masoud Ghazikor
Distributed Optimization and Control Algorithms for UAV Networks in Unlicensed Spectrum BandsWhen & Where:
Nichols Hall, Room 246 (Executive Conference Room)
Committee Members:
Morteza Hashemi, ChairVictor Frost
Prasad Kulkarni
Abstract
UAVs have emerged as a transformative technology for various applications, including emergency services, delivery, and video streaming. Among these, video streaming services in areas with limited physical infrastructure, such as disaster-affected areas, play a crucial role in public safety. UAVs can be rapidly deployed in search and rescue operations to efficiently cover large areas and provide live video feeds, enabling quick decision-making and resource allocation strategies. However, ensuring reliable and robust UAV communication in such scenarios is challenging, particularly in unlicensed spectrum bands, where interference from other nodes is a significant concern. To address this issue, developing a distributed transmission control and video streaming is essential to maintaining a high quality of service, especially for UAV networks that rely on delay-sensitive data.
In this MSc thesis, we study the problem of distributed transmission control and video streaming optimization for UAVs operating in unlicensed spectrum bands. We develop a cross-layer framework that jointly considers three inter-dependent factors: (i) in-band interference introduced by ground-aerial nodes at the physical layer, (ii) limited-size queues with delay-constrained packet arrival at the MAC layer, and (iii) video encoding rate at the application layer. This framework is designed to optimize the average throughput and PSNR by adjusting fading thresholds and video encoding rates for an integrated aerial-ground network in unlicensed spectrum bands. Using consensus-based distributed algorithm and coordinate descent optimization, we develop two algorithms: (i) Distributed Transmission Control (DTC) that dynamically adjusts fading thresholds to maximize the average throughput by mitigating trade-offs between low-SINR transmission errors and queue packet losses, and (ii) Joint Distributed Video Transmission and Encoder Control (JDVT-EC) that optimally balances packet loss probabilities and video distortions by jointly adjusting fading thresholds and video encoding rates. Through extensive numerical analysis, we demonstrate the efficacy of the proposed algorithms under various scenarios.
Ganesh Nurukurti
Customer Behavior Analytics and Recommendation System for E-CommerceWhen & Where:
Eaton Hall, Room 2001B
Committee Members:
David Johnson, ChairPrasad Kulkarni
Han Wang
Abstract
In the era of digital commerce, personalized recommendations are pivotal for enhancing user experience and boosting engagement. This project presents a comprehensive recommendation system integrated into an e-commerce web application, designed using Flask and powered by collaborative filtering via Singular Value Decomposition (SVD). The system intelligently predicts and personalizes product suggestions for users based on implicit feedback such as purchases, cart additions, and search behavior.
The foundation of the recommendation engine is built on user-item interaction data, derived from the Brazilian e-commerce Olist dataset. Ratings are simulated using weighted scores for purchases and cart additions, reflecting varying degrees of user intent. These interactions are transformed into a user-product matrix and decomposed using SVD, yielding latent user and product features. The model leverages these latent factors to predict user interest in unseen products, enabling precise and scalable recommendation generation.
To further enhance personalization, the system incorporates real-time user activity. Recent search history is stored in an SQLite database and used to prioritize recommendations that align with the user’s current interests. A diversity constraint is also applied to avoid redundancy, limiting the number of recommended products per category.
The web application supports robust user authentication, product exploration by category, cart management, and checkout simulations. It features a visually driven interface with dynamic visualizations for product insights and user interactions. The home page adapts to individual preferences, showing tailored product recommendations and enabling users to explore categories and details.
In summary, this project demonstrates the practical implementation of a hybrid recommendation strategy combining matrix factorization with contextual user behavior. It showcases the importance of latent factor modeling, data preprocessing, and user-centric design in delivering an intelligent retail experience.
Srijanya Chetikaneni
Plant Disease Prediction Using Transfer LearningWhen & Where:
Eaton Hall, Room 2001B
Committee Members:
David Johnson, ChairPrasad Kulkarni
Han Wang
Abstract
Timely detection of plant diseases is critical to safeguarding crop yields and ensuring global food security. This project presents a deep learning-based image classification system to identify plant diseases using the publicly available PlantVillage dataset. The core objective was to evaluate and compare the performance of a custom-built Convolutional Neural Network (CNN) with two widely used transfer learning models—EfficientNetB0 and MobileNetV3Small.
All models were trained on augmented image data resized to 224×224 pixels, with preprocessing tailored to each architecture. The custom CNN used simple normalization, whereas EfficientNetB0 and MobileNetV3Small utilized their respective pre-processing methods to standardize the pretrained ImageNet domain inputs. To improve robustness, the training pipeline included data augmentation, class weighting, and early stopping.
Training was conducted using the Adam optimizer and categorical cross-entropy loss over 30 epochs, with performance assessed using accuracy, loss, and training time metrics. The results revealed that transfer learning models significantly outperformed the custom CNN. EfficientNetB0 achieved the highest accuracy, making it ideal for high-precision applications, while MobileNetV3Small offered a favorable balance between speed and accuracy, making it suitable for lightweight, real-time inference on edge devices.
This study validates the effectiveness of transfer learning for plant disease detection tasks and emphasizes the importance of model-specific preprocessing and training strategies. It provides a foundation for deploying intelligent plant health monitoring systems in practical agricultural environments.
Ahmet Soyyigit
Anytime Computing Techniques for LiDAR-based Perception In Cyber-Physical SystemsWhen & Where:
Nichols Hall, Room 250 (Gemini Room)
Committee Members:
Heechul Yun, ChairMichael Branicky
Prasad Kulkarni
Hongyang Sun
Shawn Keshmiri
Abstract
The pursuit of autonomy in cyber-physical systems (CPS) presents a challenging task of real-time interaction with the physical world, prompting extensive research in this domain. Recent advances in artificial intelligence (AI), particularly the introduction of deep neural networks (DNN), have significantly improved the autonomy of CPS, notably by boosting perception capabilities.
CPS perception aims to discern, classify, and track objects of interest in the operational environment, a task that is considerably challenging for computers in a three-dimensional (3D) space. For this task, the use of LiDAR sensors and processing their readings with DNNs has become popular because of their excellent performance However, in CPS such as self-driving cars and drones, object detection must be not only accurate but also timely, posing a challenge due to the high computational demand of LiDAR object detection DNNs. Satisfying this demand is particularly challenging for on-board computational platforms due to size, weight, and power constraints. Therefore, a trade-off between accuracy and latency must be made to ensure that both requirements are satisfied. Importantly, the required trade-off is operational environment dependent and should be weighted more on accuracy or latency dynamically at runtime. However, LiDAR object detection DNNs cannot dynamically reduce their execution time by compromising accuracy (i.e. anytime computing). Prior research aimed at anytime computing for object detection DNNs using camera images is not applicable to LiDAR-based detection due to architectural differences. This thesis addresses these challenges by proposing three novel techniques: Anytime-LiDAR, which enables early termination with reasonable accuracy; VALO (Versatile Anytime LiDAR Object Detection), which implements deadline-aware input data scheduling; and MURAL (Multi-Resolution Anytime Framework for LiDAR Object Detection), which introduces dynamic resolution scaling. Together, these innovations enable LiDAR-based object detection DNNs to make effective trade-offs between latency and accuracy under varying operational conditions, advancing the practical deployment of LiDAR object detection DNNs.
Rahul Purswani
Finetuning Llama on custom data for QA tasksWhen & Where:
Eaton Hall, Room 2001B
Committee Members:
David Johnson, ChairDrew Davidson
Prasad Kulkarni
Abstract
Fine-tuning large language models (LLMs) for domain-specific use cases, such as question answering, offers valuable insights into how their performance can be tailored to specialized information needs. In this project, we focused on the University of Kansas (KU) as our target domain. We began by scraping structured and unstructured content from official KU webpages, covering a wide array of student-facing topics including campus resources, academic policies, and support services. From this content, we generated a diverse set of question-answer pairs to form a high-quality training dataset. LLaMA 3.2 was then fine-tuned on this dataset to improve its ability to answer KU-specific queries with greater relevance and accuracy. Our evaluation revealed mixed results—while the fine-tuned model outperformed the base model on most domain-specific questions, the original model still had an edge in handling ambiguous or out-of-scope prompts. These findings highlight the strengths and limitations of domain-specific fine-tuning, and provide practical takeaways for customizing LLMs for real-world QA applications.
Rithvij Pasupuleti
A Machine Learning Framework for Identifying Bioinformatics Tools and Database Names in Scientific LiteratureWhen & Where:
LEEP2, Room 2133
Committee Members:
Cuncong Zhong, ChairDongjie Wang
Han Wang
Zijun Yao
Abstract
The absence of a single, comprehensive database or repository cataloging all bioinformatics databases and software creates a significant barrier for researchers aiming to construct computational workflows. These workflows, which often integrate 10–15 specialized tools for tasks such as sequence alignment, variant calling, functional annotation, and data visualization, require researchers to explore diverse scientific literature to identify relevant resources. This process demands substantial expertise to evaluate the suitability of each tool for specific biological analyses, alongside considerable time to understand their applicability, compatibility, and implementation within a cohesive pipeline. The lack of a central, updated source leads to inefficiencies and the risk of using outdated tools, which can affect research quality and reproducibility. Consequently, there is a critical need for an automated, accurate tool to identify bioinformatics databases and software mentions directly from scientific texts, streamlining workflow development and enhancing research productivity.
The bioNerDS system, a prior effort to address this challenge, uses a rule-based named entity recognition (NER) approach, achieving an F1 score of 63% on an evaluation set of 25 articles from BMC Bioinformatics and PLoS Computational Biology. By integrating the same set of features such as context patterns, word characteristics and dictionary matches into a machine learning model, we developed an approach using an XGBoost classifier. This model, carefully tuned to address the extreme class imbalance inherent in NER tasks through synthetic oversampling and refined via systematic hyperparameter optimization to balance precision and recall, excels at capturing complex linguistic patterns and non-linear relationships, ensuring robust generalization. It achieves an F1 score of 82% on the same evaluation set, significantly surpassing the baseline. By combining rule-based precision with machine learning adaptability, this approach enhances accuracy, reduces ambiguities, and provides a robust tool for large-scale bioinformatics resource identification, facilitating efficient workflow construction. Furthermore, this methodology holds potential for extension to other technological domains, enabling similar resource identification in fields like data science, artificial intelligence, or computational engineering.
Vishnu Chowdary Madhavarapu
Automated Weather Classification Using Transfer LearningWhen & Where:
Nichols Hall, Room 246 (Executive Conference Room)
Committee Members:
David Johnson, ChairPrasad Kulkarni
Dongjie Wang
Abstract
This project presents an automated weather classification system utilizing transfer learning with pre-trained convolutional neural networks (CNNs) such as VGG19, InceptionV3, and ResNet50. Designed to classify weather conditions—sunny, cloudy, rainy, and sunrise—from images, the system addresses the challenge of limited labeled data by applying data augmentation techniques like zoom, shear, and flip, expanding the dataset images. By fine-tuning the final layers of pre-trained models, the solution achieves high accuracy while significantly reducing training time. VGG19 was selected as the baseline model for its simplicity, strong feature extraction capabilities, and widespread applicability in transfer learning scenarios. The system was trained using the Adam optimizer and evaluated on key performance metrics including accuracy, precision, recall, and F1 score. To enhance user accessibility, a Flask-based web interface was developed, allowing real-time image uploads and instant weather classification. The results demonstrate that transfer learning, combined with robust data preprocessing and fine-tuning, can produce a lightweight and accurate weather classification tool. This project contributes toward scalable, real-time weather recognition systems that can integrate into IoT applications, smart agriculture, and environmental monitoring.
RokunuzJahan Rudro
Using Machine Learning to Classify Driver Behavior from Psychological Features: An Exploratory StudyWhen & Where:
Eaton Hall, Room 1A
Committee Members:
Sumaiya Shomaji, ChairDavid Johnson
Zijun Yao
Alexandra Kondyli
Abstract
Driver inattention and human error are the primary causes of traffic crashes. However, little is known about the relationship between driver aggressiveness and safety. Although several studies that group drivers into different classes based on their driving performance have been conducted, little has been done to explore how behavioral traits are linked to driver behavior. The study aims to link different driver profiles, assessed through psychological evaluations, with their likelihood of engaging in risky driving behaviors, as measured in a driving simulation experiment. By incorporating psychological factors into machine learning algorithms, our models were able to successfully relate self-reported decision-making and personality characteristics with actual driving actions. Our results hold promise toward refining existing models of driver behavior by understanding the psychological and behavioral characteristics that influence the risk of crashes.
Md Mashfiq Rizvee
Energy Optimization in Multitask Neural Networks through Layer SharingWhen & Where:
Eaton Hall, Room 2001B
Committee Members:
Sumaiya Shomaji, ChairTamzidul Hoque
Han Wang
Abstract
Artificial Intelligence (AI) is being widely used in diverse domains such as industrial automation, traffic control, precision agriculture, and smart cities for major heavy lifting in terms of data analysis and decision making. However, the AI life- cycle is a major source of greenhouse gas (GHG) emission leading to devastating environmental impact. This is due to expensive neural architecture searches, training of countless number of models per day across the world, in-field AI processing of data in billions of edge devices, and advanced security measures across the AI life cycle. Modern applications often involve multitasking, which involves performing a variety of analyzes on the same dataset. These tasks are usually executed on resource-limited edge devices, necessitating AI models that exhibit efficiency across various measures such as power consumption, frame rate, and model size. To address these challenges, we introduce a novel neural network architecture model that incorporates a layer sharing principle to optimize the power usage. We propose a novel neural architecture, Layer Shared Neural Networks that merges multiple similar AI/NN tasks together (with shared layers) towards creating a single AI/NN model with reduced energy requirements and carbon footprint. The experimental findings reveal competitive accuracy and reduced power consumption. The layer shared model significantly reduces power consumption by 50% during training and 59.10% during inference causing as much as an 84.64% and 87.10% decrease in CO2 emissions respectively.
Fairuz Shadmani Shishir
Parameter-Efficient Computational Drug Discovery using Deep LearningWhen & Where:
Eaton Hall, Room 2001B
Committee Members:
Sumaiya Shomaji, ChairTamzidul Hoque
Hongyang Sun
Abstract
The accurate prediction of small molecule binding affinity and toxicity remains a central challenge in drug discovery, with significant implications for reducing development costs, improving candidate prioritization, and enhancing safety profiles. Traditional computational approaches, such as molecular docking and quantitative structure-activity relationship (QSAR) models, often rely on handcrafted features and require extensive domain knowledge, which can limit scalability and generalization to novel chemical scaffolds. Recent advances in language models (LMs), particularly those adapted to chemical representations such as SMILES (Simplified Molecular Input Line Entry System), have opened new ways for learning data-driven molecular representations that capture complex structural and functional properties. However, achieving both high binding affinity and low toxicity through a resource-efficient computational pipeline is inherently difficult due to the multi-objective nature of the task. This study presents a novel dual-paradigm approach to critical challenges in drug discovery: predicting small molecules with high binding affinity and low cardiotoxicity profiles. For binding affinity prediction, we implement a specialized graph neural network (GNN) architecture that operates directly on molecular structures represented as graphs, where atoms serve as nodes and bonds as edges. This topology-aware approach enables the model to capture complex spatial arrangements and electronic interactions critical for protein-ligand binding. For toxicity prediction, we leverage chemical language models (CLMs) fine-tuned with Low-Rank Adaptation (LoRA), allowing efficient adaptation of large pre-trained models to specialized toxicological endpoints while maintaining the generalized chemical knowledge embedded in the base model. Our hybrid methodology demonstrates significant improvements over existing computational approaches, with the GNN component achieving an average area under the ROC curve (AUROC) of 0.92 on three protein targets and the LoRA-adapted CLM reaching (AUROC) of 0.90 with 60% reduction in parameter usage in predicting cardiotoxicity. This work establishes a powerful computational framework that accelerates drug discovery by enabling both higher binding affinity and low toxicity compounds with optimized efficacy and safety profiles.
Soma Pal
Truths about compiler optimization for state-of-the-art (SOTA) C/C++ compilersWhen & Where:
Eaton Hall, Room 2001B
Committee Members:
Prasad Kulkarni, ChairEsam El-Araby
Drew Davidson
Tamzidul Hoque
Jiang Yunfeng
Abstract
Compiler optimizations are critical for performance and have been extensively studied, especially for C/C++ language compilers. Our overall goal in this thesis is to investigate and compare the properties and behavior of optimization passes across multiple contemporary, state-of-the-art (SOTA) C/C++ compilers to understand if they adopt similar optimization implementation and orchestration strategies. Given the maturity of pre-existing knowledge in the field, it seems conceivable that different compiler teams will adopt consistent optimization passes, pipeline and application techniques. However, our preliminary results indicate that such expectation may be misguided. If so, then we will attempt to understand the differences, and study and quantify their impact on the performance of generated code.
In our first work, we study and compare the behavior of profile-guided optimizations (PGO) in two popular SOTA C/C++ compilers, GCC and Clang. This study reveals many interesting, and several counter-intuitive, properties about PGOs in C/C++ compilers. The behavior and benefits of PGOs also vary significantly across our selected compilers. We present our observations, along with plans to further explore these inconsistencies in this report. Likewise, we have also measured noticeable differences in the performance delivered by optimizations across our compilers. We propose to explore and understand these differences in this work. We present further details regarding our proposed directions and planned experiments in this report. We hope that this work will show and suggest opportunities for compilers to learn from each other and motivate researchers to find mechanisms to combine the benefits of multiple compilers to deliver higher overall program performance.
Nyamtulla Shaik
AI Vision to Care: A QuadView of Deep Learning for Detecting Harmful Stimming in AutismWhen & Where:
Eaton Hall, Room 2001B
Committee Members:
Sumaiya Shomaji, ChairBo Luo
Dongjie Wang
Abstract
Stimming refers to repetitive actions or behaviors used to regulate sensory input or express feelings. Children with developmental disorders like autism (ASD) frequently perform stimming. This includes arm flapping, head banging, finger flicking, spinning, etc. This is exhibited by 80-90% of children with Autism, which is seen in 1 among 36 children in the US. Head banging is one of these self-stimulatory habits that can be harmful. If these behaviors are automatically identified and notified using live video monitoring, parents and other caregivers can better watch over and assist children with ASD.
Classifying these actions is important to recognize harmful stimming, so this study focuses on developing a deep learning-based approach for stimming action recognition. We implemented and evaluated four models leveraging three deep learning architectures based on Convolutional Neural Networks (CNNs), Autoencoders, and Vision Transformers. For the first time in this area, we use skeletal joints extracted from video sequences. Previous works relied solely on raw RGB videos, vulnerable to lighting and environmental changes. This research explores Deep Learning based skeletal action recognition and data processing techniques for a small unstructured dataset that consists of 89 home recorded videos collected from publicly available sources like YouTube. Our robust data cleaning and pre-processing techniques helped the integration of skeletal data in stimming action recognition, which performed better than state-of-the-art with a classification accuracy of up to 87%
In addition to using traditional deep learning models like CNNs for action recognition, this study is among the first to apply data-hungry models like Vision Transformers (ViTs) and Autoencoders for stimming action recognition on the dataset. The results prove that using skeletal data reduces the processing time and significantly improves action recognition, promising a real-time approach for video monitoring applications. This research advances the development of automated systems that can assist caregivers in more efficiently tracking stimming activities.
Alexander Rodolfo Lara
Creating a Faradaic Efficiency Graph Dataset Using Machine LearningWhen & Where:
Eaton Hall, Room 2001B
Committee Members:
Zijun Yao, ChairSumaiya Shomaji
Kevin Leonard
Abstract
Just as the internet-of-things leverages machine learning over a vast amount of data produced by an innumerable number of sensors, the Internet of Catalysis program uses similar strategies with catalysis research. One application of the Internet of Catalysis strategy is treating research papers as datapoints, rich with text, figures, and tables. Prior research within the program focused on machine learning models applied strictly over text.
This project is the first step of the program in creating a machine learning model from the images of catalysis research papers. Specifically, this project creates a dataset of faradaic efficiency graphs using transfer learning from pretrained models. The project utilizes FasterRCNN_ResNet50_FPN, LayoutLMv3SequenceClassification, and computer vision techniques to recognize figures, extract all graphs, then classify the faradaic efficiency graphs.
Downstream of this project, researchers will create a graph reading model to integrate with large language models. This could potentially lead to a multimodal model capable of fully learning from images, tables, and texts of catalysis research papers. Such a model could then guide experimentation on reaction conditions, catalysts, and production.
Amin Shojaei
Scalable and Cooperative Multi-Agent Reinforcement Learning for Networked Cyber-Physical Systems: Applications in Smart GridsWhen & Where:
Nichols Hall, Room 246 (Executive Conference Room)
Committee Members:
Morteza Hashemi, ChairAlex Bardas
Prasad Kulkarni
Taejoon Kim
Shawn Keshmiri
Abstract
Significant advances in information and networking technologies have transformed Cyber-Physical Systems (CPS) into networked cyber-physical systems (NCPS). A noteworthy example of such systems is smart grid networks, which include distributed energy resources (DERs), renewable generation, and the widespread adoption of Electric Vehicles (EVs). Such complex NCPS require intelligent and autonomous control solutions. For example, the increasing number of EVs introduces significant sources of demand and user behavior uncertainty that can jeopardize grid stability during peak hours. Traditional model-based demand-supply controls fail to accurately model and capture the complex nature of smart grid systems in the presence of different uncertainties and as the system size grows. To address these challenges, data-driven approaches have emerged as an effective solution for informed decision-making, predictive modeling, and adaptive control to enhance the resiliency of NCPS in uncertain environments.
As a powerful data-driven approach, Multi-Agent Reinforcement Learning (MARL) enables agents to learn and adapt in dynamic and uncertain environments. However, MARL techniques introduce complexities related to communication, coordination, and synchronization among agents. In this PhD research, we investigate autonomous control for smart grid decision networks using MARL. First, we examine the issue of imperfect state information, which frequently arises due to the inherent uncertainties and limitations in observing the system state.
Second, we focus on the cooperative behavior of agents in distributed MARL frameworks, particularly under the central training with decentralized execution (CTDE) paradigm. We provide theoretical results and variance analysis for stochastic and deterministic cooperative MARL algorithms, including Multi-Agent Deep Deterministic Policy Gradient (MADDPG), Multi-Agent Proximal Policy Optimization (MAPPO), and Dueling MAPPO. These analyses highlight how coordinated learning can improve system-wide decision-making in uncertain and dynamic environments like EV networks.
Third, we address the scalability challenge in large-scale NCPS by introducing a hierarchical MARL framework based on a cluster-based architecture. This framework organizes agents into coordinated subgroups, improving scalability while preserving local coordination. We conduct a detailed variance analysis of this approach to demonstrate its effectiveness in reducing communication overhead and learning complexity. This analysis establishes a theoretical foundation for scalable and efficient control in large-scale smart grid applications.
Past Defense Notices
AISHWARYA BHATNAGAR
Autonomous surface detection and tracking for FMCW Snow Radar using field programmable gate arraysWhen & Where:
317 Nichols Hall
Committee Members:
Carl Leuschen, ChairChristopher Allen
Fernando Rodriguez-Morales
Abstract
Sea ice in polar regions is typically covered with a layer of snow. The thermal insulation properties and high albedo of the snow cover insulates the sea ice beneath it, maintaining low temperatures and limiting ice melt, and thus affecting sea ice thickness and growth rates. Remote sensing of snow cover thickness plays a major role in understanding the mass balance of sea ice, inter-annual variability of snow depth, and other factors which directly impact climate change. The Center for Remote Sensing of Ice Sheets (CReSIS) at the University of Kansas has developed an ultra-wide band FMCW Snow Radar used to measure snow thickness and map internal layers of polar firn. The radar’s deployment on high-endurance, fixed-wing aircraft makes it difficult to track the surface from these platforms, due to turbulence and a limited range window. In this thesis, an automated onboard real-time surface tracker for the snow radar is presented to detect the snow surface elevation from the aircraft and track changes in the surface elevation. For an FMCW radar to have a long-range (high altitude) capability, a reference chirp delaying ability is a necessity to maintain a relatively constant beat frequency. Currently, the radar uses a filter bank to bandpass the received IF signal and store the spectral power in each band by utilizing different Nyquist zones. During airborne missions in polar regions with the radar, the operator has to manually switch the filter banks one by one as the aircraft elevation from the surface increases. The work done in this thesis aims at eliminating the manual switching operation and providing the radar with surface detection, chirp delay, and a constant beat frequency feedback loop in order to enhance its long range capability and ensure autonomous operation.
Xinyang Rui
Performance Analysis of Mobile ad hoc Network Routing Protocols Using ns-3 SimulationsWhen & Where:
246 Nichols Hall
Committee Members:
James Sterbenz , ChairBo Luo
Gary Minden
Abstract
Mobile ad hoc networks (MANETs) consist of mobile nodes that can communicate with each other through wireless links without the help of any infrastructure. The dynamic topology of MANETs poses a significant challenge for the design of routing protocols. Many routing protocols have been developed to discover routes in MANETs through different mechanisms such as source routing and link state routing. In this thesis, we present a comprehensive performance analysis of several prominent MANET routing protocols. The protocols studied are Destination Sequenced Distance Vector protocol (DSDV), Optimized Link State Routing protocol (OLSR), Ad hoc On-demand Distance Vector protocol (AODV), and Dynamic Source Routing (DSR). We evaluate their performance on metrics such as packet delivery ratio, end-to-end delay, and routing overhead through simulations in different scenarios with ns-3. These scenarios involve different node density, node velocity, and mobility models including Steady-State Random Waypoint, Gauss-Markov, and Lévy Walk. We believe this study will be helpful for the understanding of mobile routing dynamics, the improvement of current MANET routing protocols, and the development of new protocols.
ALI ALSHAWISH
A New Fault-Tolerant Topology and Operation Scheme for the High Voltage Stage in a Three-Phase Solid-State TransformerWhen & Where:
1 Eaton Hall
Committee Members:
Reza Ahmadi, ChairTaejoon Kim
Glenn Prescott
Alessandro Salandrino
Elaina Sutley
Abstract
One of the most important reliability concerns for Solid-State Transformers (SST) is related to high voltage side switch and grid faults. High voltage stress on the switches, together with the fact that most modern SST topologies comprise a large number of power switches in the high voltage side, contribute to a higher probability of a switch fault occurrence. Furthermore, high voltage grid faults that result in unbalanced operating conditions in SSTs can lead to more dire consequences in regards to safety and reliability in comparison to traditional transformers. This work proposes a new SST topology in conjunction with a fault-tolerant operation strategy that can fully restore operation of the proposed SST in case of the two mentioned fault scenarios. Also, the proposed SST is a new topology to generate three-phase voltages from two-phase voltages, and it is designed to increase the lifetime of the proposed SST.
SUSANNA MOSLEH
Multi-user MIMO Networks: Resource Allocation and Interference MitigationWhen & Where:
246 Nichols Hall
Committee Members:
Erik Perrins, ChairShannon Blunt
Victor Frost
Lingjia Liu
Jian Li
Abstract
Nowadays, wireless communications are becoming so tightly integrated in our daily lives, especially with the global spread of laptops, tablets and smartphones. This has paved the way to dramatically increasing wireless network dimensions in terms of subscribers and amount of flowing data. The two important fundamental requirements for the future 5G wireless networks are abilities to support high data traffic and exceedingly low latency. A likely candidate to fulfill these requirements is multi-cell multi-user multi-input multiple-output (MIMO); also termed as coordinated multipoint (CoMP) transmission and reception. In order to achieve the highest possible performance of this aforementioned candidate technology, a properly designed resource allocation algorithm is needed. By designing a resource allocation algorithm which maximizes the network throughput, this technology is able to manage the exponential growth of wireless network dimensions. Moreover, with the rapidly growing data traffic, interference has become a major limitation in wireless networks. To deal with this issue and in order to manage the interference in the wireless network systems, various interference mitigation techniques have been introduced among which interference alignment (IA) has been shown to significantly improve the network performance. However, how to practically use IA to mitigate inter-cell interference in a downlink multi-cell multi-user MIMO networks still remains an open problem. To address the above listed problems, in this dissertation we improve the performance of wireless networks, in terms of spectral efficiency, by developing new algorithms and protocols that can efficiently mitigate the interference and allocate the resources. In particular, we will focus on designing new beamforming algorithms in downlink multi-cell multi-user MIMO networks. Furthermore, we mathematically analyze the performance improvement of multi-user MIMO networks employing proposed techniques. Fundamental relationships between network parameters and the network performance will be revealed, which will provide guidance on the wireless networks design. Finally, the results of theoretical study will be demonstrated using MATLAB.
KISHANRAM KAJE
Complex Field Modulation in Direct Detection SystemsWhen & Where:
246 Nichols Hall
Committee Members:
Rongqing Hui, ChairChristopher Allen
Victor Frost
Erik Perrins
Siyuan Han
Abstract
Even though fiber optics communication is providing a high bandwidth channel to achieve high speed data transmission, there is still a need for higher spectral efficiency, faster data processing speeds while reduced resource requirements due to ever increasing data and media traffic. Various multilevel modulation and demodulation techniques are used to improve spectral efficiency. Although, spectral efficiency is improved, there are other challenges that arise while doing so such as requirement for high speed electronics, receiver sensitivity, chromatic dispersion, operational flexibility etc. Here, we investigate multilevel modulation techniques to improve spectral efficiency while reducing the resource requirements.
We demonstrated a digital-analog hybrid subcarrier multiplexing (SCM) technique which can reduce the requirement of high speed electronics such as ADC and DAC, while providing wideband capability, high spectral efficiency, operational flexibility and controllable data-rate granularity.
With conventional Quadrature Phase Shift Keying (QPSK), to achieve maximum spectral efficiency, we need high spectral efficient Nyquist filters which takes high FPGA resources for digital signal processing (DSP). Hence, we investigated Quadrature Duobinary (QDB) modulation as a solution to reduce the FPGA resources required for DSP while achieving spectral efficiency of 2bits/s/Hz. Currently we are investigating all analog single sideband (SSB) complex field modulated direct detection system. Here, we are trying to achieve higher spectral efficiency by using QDB modulation scheme in comparison to QPSK while avoiding signal-signal beat interference (SSBI) by providing a guard-band based approach.
In coherent detection systems, the MLSE receiver could be implemented using Viterbi algorithm. However, in case of direct detection systems due to square law detection the noise in the received signal is not Gaussian anymore. This leads to requirement of channel behavior estimation for the implementation of MLSE receiver in direct detection systems. Recently, Kramers-Kronig receiver has attracted great deal of attention. We are working on utilizing Kramers-Kronig receiver to implement MLSE receiver for direct detection system without the need for channel estimation.
MAHDI JAFARISHIADEH
New Topology and Improved Control of Modular Multilevel Converter (MMC)-Based ConvertersWhen & Where:
1 Eaton Hall
Committee Members:
Reza Ahmadi, ChairGlenn Prescott
Alessandro Salandrino
James Stiles
Xiaoli (Laura) Li
Abstract
Trends toward large-scale integration and the high-power application of green energy resources necessitate the advent of efficient power converter topologies, multilevel converters. Multilevel inverters are effective solutions for high power and medium voltage DC-to-AC conversion due to their higher efficiency, provision of system redundancy, and generation of near-sinusoidal output voltage waveform. Among many proposed multilevel topologies, the neutral-point-clamped (NPC), flying capacitor (FC), and cascaded H-bridge (CHB) converters are the most well-known classical multilevel topologies. For generation of output voltages with more than five levels, the number of required diodes and capacitors in NPC and FC increases rapidly. Also, these two topologies suffer from a significant capacitor voltage balancing problem. CHBs also require bulky multi-winding transformers to realize several isolated dc sources. Recently, modular multilevel converter (MMC) has become increasingly attractive due to its modularity, high efficiency, excellent output voltage waveform, and no need for separate dc sources. To improve the harmonic profile of the output voltage, there is the need to increase the number of output voltage levels. However, this would require increasing the number of submodules (SMs) and power semi-conductor devices and their associated gate driver and protection circuitry, resulting in the overall multilevel converter to be complex and expensive. Fewer efforts have been devoted to proposing MMC-based multilevel topologies focusing on reduced part count. This work will investigate new medium-voltage high-power MMC-based multilevel inverter with reduced component numbers while using conventional half-bridge SM structure.
The second part of this work is on improving control of MMC-based high-power DC-DC converters. Medium-voltage DC (MVDC) grids have been the focus of numerous research studies in recent years due to their increasing applications in rapidly growing grid-connected renewable energy systems, such as wind, solar and wave farms. MMC-based DC-DC converters are employed for collecting power from offshore wind and wave farms. Among various developed high-power DC-DC converter topologies, MMC-based DC-DC converter with medium-frequency (MF) transformer is a valuable topology due to its numerous advantages. Specifically, they offer a significant reduction in the size of the MMC arm capacitors along with the ac-link transformer and arm inductors due to the ac-link transformer operating at medium frequencies. As such, this work focuses on improving the control of isolated MMMC-based DC-DC converters. Conventionally, the active power is controlled by phase shifts between the primary side and secondary side of transformers. Through this work, adding degree of freedom is investigated by considering the amplitude ratio index of MMC leg as a single control parameter. From the derived analytical formulas, this will lead to operating points where the same active power is transferrable but current stress is reduced. Subsequently, longer lifetimes of the high-frequency transformer and power switches are expected.
The specific goals of this work are, (1) Investigating new topology of MMC-based inverter that generate the same peak-to-peak output voltage and voltage levels as conventional MMC but require fewer components. (2) Improving control of isolated MMC-based DC-DC converters to reduce the current stress of the switches and transformer while delivering same power.
RAVALI KONDREDDI
LocTrac - Android application for location trackingWhen & Where:
2001 B Eaton Hall
Committee Members:
Jerzy Grzymala-Busse, ChairMan Kong
Prasad Kulkarni
Abstract
Owing a mobile phone has come to be regarded as a necessity in today’s world. Smart phone is an effective way to locate a person anywhere in this world. Android is an open source software stack with the largest number of users. Hence, this application is developed in Android. LocTrac is an Android application used to track the location of the user. During the time of emergencies or accidents, a person may not be in a situation to let others know about his/her location. LocTrac is an application which automatically send the user’s location to registered contacts so that they can track him/her down. In this application we initially register few contacts as guardians, when the user doesn’t answer the call, his/her location is automatically sent to the registered contacts. This application also uses sensors to capture the phone movement and send the location. Timer, alarm, emergency call are other features of this application.
NIDHI MIDHA
Study of k-Fold Cross ValidationWhen & Where:
2001 B Eaton Hall
Committee Members:
Jerzy Grzymala-Busse, ChairJohn Garrett Morris
Heechul Yun
Abstract
Enormous amount of data is being generated due to advancement in technology. The basic question of discovering knowledge from the data generated is still pertinent. Data mining guides us in discovering patterns or rules. Various techniques are applied to find the error rate on testing data sets based on rules generated from stratified training data sets. In this project, using the k-Fold Cross Validation approach, we vary the number of folds the training data set is divided into, stratify the folds, and find the error rates on testing data sets for each ‘k’. For every data set in each k, experiment is repeated certain number of times such that there is a random testing data set each time. We observed that as the value of k increases, the error rate starts getting stabilized, and there is a stage when error rate doesn't increase even if we increase the number of folds.
ABDULMALIK HUMAYED
Securing CAN-Based Cyber-Physical SystemsWhen & Where:
246 Nichols Hall
Committee Members:
Bo Luo, ChairArvin Agah
Prasad Kulkarni
Heechul Yun
Prajna Dhar
Abstract
With the exponential growth of cyber-physical systems (CPSs), new security challenges have emerged. Various vulnerabilities, threats, attacks, and controls have been introduced for the new generation of CPS. However, there lacks a systematic review of the CPS security literature. In particular, the heterogeneity of CPS components and the diversity of CPS systems have made it difficult to study the problem with one generalized model. As the first component of this dissertation, existing research on CPS security is studied and systematized under a unified framework. Smart cars, as a CPS application, was further explored under the proposed framework and new attacks are identified and addressed.
The Control Area Network (CAN bus) is a prevalent serial communication protocol adopted in industrial CPS, especially in small and large vehicles, ships, planes, and even in drones, radar systems, and submarines. Unfortunately, the CAN bus was designed without any security considerations. We then propose and demonstrate a stealthy targeted Denial of Service (DoS) attack against CAN. Experimentations show that the attack is effective and superior to attacks of the same category due to its stealthiness and ability to avoid detection from current countermeasures.
Two controls are proposed to defend against various spoofing and DoS attacks on CAN. The first one aims to minimize the attack using ID-Hopping mechanism such that CAN arbitration IDs are randomized so an attacker would not be able to target them. ID-Hopping raises the bar for attackers by randomizing the expected patterns in CAN network. Such randomization hinders the attacker's ability to launch targeted DoS attacks. Based on the evaluation on the testbed, the randomization mechanism, ID-Hopping, holds a promising solution for targeted DoS, and reverse engineering CAN IDs, which CAN networks are most vulnerable to. The second countermeasure is a novel CAN firewall that aims to prevent an attacker from launching a plethora of untraditional attacks on CAN that existing solutions do not adequately address. The firewall is placed between a potential attacker’s node and the rest of the CAN bus. Traffic is controlled bidirectionally between the main bus and the attacker’s side so that only benign traffic can pass to the main bus. This ensures that an attacker cannot arbitrarily inject malicious traffic into the main bus. Demonstration and evaluation of the attack and firewall were conducted by a bit-level analysis, i.e., “Bit banging”, of CAN’s traffic. Results show that the firewall successfully prevents the stealthy targeted DoS attack, as well as, other recent attacks. To evaluate the proposed attack and firewall, a testbed was built that consists of BeagleBone Black and STM32 Nucleo-144 microcontrollers to simulate real CAN traffic.
Finally, a design of an Intrusion Detection System (IDS) is proposed to complement the firewall. It utilizes the proposed firewall to add situational awareness capabilities to the bus’s security posture and detect and react to attacks that might bypass the firewall based on certain rules.
SAIKAT SENGUPTA
Understanding Memory Access Behavior for Heterogeneous Memory SystemsWhen & Where:
2001 B Eaton Hall
Committee Members:
Prasad Kulkarni, ChairPerry Alexander
Jerzy W. Grzymala-Busse
Abstract
Present day manufacturers have invented different memory technologies with distinct bandwidth, energy and cost tradeoffs. Systems with such heterogeneous memory technologies can only achieve the best performance and power characteristics by appropriately partitioning process data on OS pages and placing OS pages in the right memory areas. To achieve effective data partitioning and placement we need to first understand how programs access memory and how those patterns change at various stages (phases) of program execution. The goal of this work is to build a framework, design experiments and conduct analysis to understand overall memory usage patterns across many programs.
We use Intel’s Pin dynamic binary translation and instrumentation system for this work. Our Pin based framework instruments programs at run-time to collect data regarding memory allocations, de-allocations, reads and writes, which we then analyze using our specialized scripts. We collect and analyze information including page access counts, hot page ratio, memory read and write access patterns and how that varies in different program phases. We also analyze the similarities regarding memory behavior between distinct phases during program execution. We also study memory behavior both with cache and without cache to understand how caches affect the memory access behavior.