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
Lei Wang
I Know What You Type on Your Phone: Keystroke Inference on Android Device Using Deep LearningWhen & Where:
246 Nichols Hall
Committee Members:
Bo Luo, ChairFengjun Li
Guanghui Wang
Abstract
Given a list of smartphone sensor readings, such as accelerometer, gyroscope and light sensor, is there enough information present to predict a user’s input without access to either the raw text or keyboard log? With the increasing usage of smartphones as personal devices to access sensitive information on-the-go has put user privacy at risk. As the technology advances rapidly, smartphones now equip multiple sensors to measure user motion, temperature and brightness to provide constant feedback to applications in order to receive accurate and current weather forecast, GPS information and so on. In the ecosystem of Android, sensor reading can be accessed without user permissions and this makes Android devices vulnerable to various side-channel attacks.
In this thesis, we first create a native Android app to collect approximately 20700 keypresses from 30 volunteers. The text used for the data collection is carefully selected based on the bigram analysis we run on over 1.3 million tweets. We then present two approaches (single key press and bigram) for feature extraction, those features are constructed using accelerometer, gyroscope and light sensor readings. A deep neural network with four hidden layers is proposed as the baseline for this work, which achieves an accuracy of 47% using categorical cross entropy as the accuracy metric. A multi-view model then is proposed in the later work and multiple views are extracted and performance of the combination of each view is compared for analysis.
Wenchi Ma
Deep Neural Network based Object Detection and Regularization in Deep LearningWhen & Where:
246 Nichols Hall
Committee Members:
Richard Wang, ChairArvin Agah
Bo Luo
Heechul Yun
Haiyang Chao
Abstract
The abilities of feature learning, scene understanding, and task generalization are the consistent pursuit in deep learning-based computer vision. A number of object detectors with various network structures and algorithms have been proposed to learn more effective features, to extract more contextual and semantic information, and to achieve more robust and more accurate performance on different datasets. Nevertheless, the problem is still not well addressed in practical applications. One major issue lies in the inefficient feature learning and propagation in challenging situations like small objects, occlusion, illumination, etc. Another big issue is the poor generalization ability on datasets with different feature distribution.
The study aims to explore different learning frameworks and strategies to solve the above issues. (1) We propose a new model to make full use of different features from details to semantic ones for better detection of small and occluded objects. The proposed model emphasizes more on the effectiveness of semantic and contextual information from features produced in high-level layers. (2) To achieve more efficient learning, we propose the near-orthogonality regularization, which takes the neuron redundancy into consideration, to generate better deep learning models. (3) We are currently working on tightening the object localization by integrating the localization score into a non-maximum suppression (NMS) to achieve more accurate detection results, and on the domain adaptive learning that encourages the learning models to acquire higher generalization ability of domain transfer.
MAHDI JAFARISHIADEH
New Topology and Improved Control of Modular Multilevel Based ConvertersWhen & Where:
2001 B 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. Recently, modular multilevel converter (MMC) has become increasingly attractive. 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. Specifically, the need for large number of bulky capacitors in SMs of conventional MMC is seen as a major obstacle. This work proposes an MMC-based multilevel converter that provides the same output voltage as conventional MMC but has reduced number of bulky capacitors. This is achieved by introduction of an extra middle arm to the conventional MMC. Due to similar dynamic equations of the proposed converter with conventional MMC, several previously developed control methods for voltage balancing in the literature for conventional MMCs are applicable to the proposed MMC with minimal effort. Comparative loss analysis of the conventional MMC and the proposed multilevel converter under different power factors and modulation indexes illustrates the lower switching loss of proposed MMC. In addition, a new voltage balancing technique based on carrier-disposition pulse width modulation for modular multilevel converter is proposed.
The second part of this work focuses on an improved control of MMC-based high-power DC/DC converters. Medium-voltage DC (MVDC) and high-voltage DC (HVDC) 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 and solar farms. MMC-based DC/DC converters are employed for collecting power from renewable energy sources. Among various developed 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 MMC-based DC/DC (IMMDC) converters. The single phase shift (SPS) control is a popular method in IMMDC converter to control the power transfer. This work proposes conjoined phase shift-amplitude ratio index (PSAR) control that considers amplitude ratio indexes of MMC legs of MF transformer’s secondary side as additional control variables. Compared with SPS control, PSAR control not only provides wider transmission power range and enhances operation flexibility of converter, but also reduces current stress of medium-frequency transformer and power switches of MMCs. An algorithm is developed for simple implementation of the PSAR control to work at the least current stress operating point. Hardware-in-the-loop results confirm the theoretical outcomes of the proposed control method.
Luyao Shang
Memory Based Luby Transform Codes for Delay Sensitive Communication SystemsWhen & Where:
246 Nichols Hall
Committee Members:
Erik Perrins, ChairShannon Blunt
Taejoon Kim
David Petr
Tyrone Duncan
Abstract
As the upcoming fifth-generation (5G) and future wireless network is envisioned in areas such as augmented and virtual reality, industrial control, automated driving or flying, robotics, etc, the requirement of supporting ultra-reliable low-latency communications (URLLC) is increasingly urgent than ever. From the channel coding perspective, URLLC requires codewords being transported in finite block-lengths. In this regards, we propose novel encoding algorithms and analyze their performance behaviors for the finite-length Luby transform (LT) codes.
Luby transform (LT) codes, the first practical realization and the fundamental core of fountain codes, play a key role in the fountain codes family. Recently, researchers show that the performance of LT codes for finite block-lengths can be improved by adding memory into the encoder. However, this work only utilizes one memory, leaving the possibilities of exploiting and how to exploiting more memories an open problem. To explore this unknown, this proposed research targets to 1) propose an encoding algorithm to utilize one more memory and compare its performance with the existing work; 2) generalize the memory based encoding method to arbitrary memory orders and mathematically analyze its performance; 3) find out the optimal memory order in terms of bit error rate (BER), frame error rate (FER), and decoding convergence speed; 4) Apply the memory based encoding algorithm to additive white Gaussian noise (AWGN) channels and analyze its performance.
Saleh Mohamed Eshtaiwi
A New Three Phase Photovoltaic Energy Harvesting System for Generation of Balanced Voltages in Presence of Partial Shading, Module Mismatch, and Unequal Maximum Power PointsWhen & Where:
2001 B Eaton Hall
Committee Members:
Reza Ahmadi , ChairChristopher Allen
Jerzy Grzymala-Busse
Rongqing Hui
Elaina Sutley
Abstract
The worldwide energy demand is growing quickly, with an anticipated rate of growth of 48% from 2012 to 2040. Consequently, investments in all forms of renewable energy generation systems have been growing rapidly. Increased use of clean renewable energy resources such as hydropower, wind, solar, geothermal, and biomass is expected to noticeably renewable energy resources alleviate many present environmental concerns associated with fossil fuel-based energy generation. In recent years, wind and solar energies are gained the most attention among all other renewable resources. As a result, both have become the target of extensive research and development for dynamic performance optimization, cost reduction, and power reliability assurance.
The performance of Photovoltaic (PV) systems is highly affected by environmental and ambient conditions such as irradiance fluctuations and temperature swings. Furthermore, the initial capital cost for establishing the PV infrastructure is very high. Therefore, its essential that the PV systems always harvest the maximum energy possible by operating at the most efficient operating point, i.e. Maximum Power Point (MPP), to increase conversion efficiency and thus result in lowest cost of captured energy.
The dissertation is an effort to develop a new PV conversion system for large scale PV grid-connected systems which provides efficacy enhancements compared to conventional systems by balancing voltage mismatches between the PV modules. Hence, it analyzes the theoretical models for three selected DC/DC converters. To accomplish this goal, this work first introduces a new adaptive maximum PV energy extraction technique for PV grid-tied systems. Then, it supplements the proposed technique with a global search approach to distinguish absolute maximum power peaks within multi-peaks in case of partially shaded PV module conditions. Next, it proposes an adaptive MPP tracking (MPPT) strategy based on the concept of model predictive control (MPC) in conjunction with a new current sensor-less approach to reduce the number of required sensors in the system. Finally, this work proposes a power balancing technique for injection of balanced three-phase power into the grid using a Cascaded H-Bridge (CHB) converter topology which brings together the entire system and results in the final proposed PV power system. The resulting PV system offers enhanced reliability by guaranteeing effective system operation under unbalanced phase voltages caused by severe partial shading.
The developed grid connected PV solar system is evaluated using simulations under realistic dynamic ambient conditions, partial shading, and fully shading conditions and the obtained results confirm its effectiveness and merits comparted to conventional systems.
Shruti Goel
DDoS Intrusion Detection using Machine Learning TechniquesWhen & Where:
250 Nichols Hall
Committee Members:
Alex Bardas, ChairFengjun Li
Bo Luo
Abstract
Organizations are becoming more exposed to security threats due to shift towards cloud infrastructure and IoT devices. One growing category of cyber threats is Distributes Denial of Service (DDoS) attacks. It is hard to detect DDoS attacks due to evolving attack patterns and increasing data volume. So, creating filter rules manually to distinguish between legitimate and malicious traffic is a complex task. Current work explores a supervised machine learning based approach for DDoS detection. The proposed model uses a step forward feature selection method to extract 15 best network features and random forest classifier for detecting DDoS traffic. This solution can be used as an automatic detection algorithm for DDoS mitigation pipelines implemented in the most up-to-date DDoS security solutions.
Hayder Almosa
Downlink Achievable Rate Analysis for FDD Massive MIMO SystemsWhen & Where:
129 Nichols Hall
Committee Members:
Erik Perrins , ChairLingjia Liu
Shannon Blunt
Rongqing Hui
Hongyi Cai
Abstract
Multiple-Input Multiple-Output (MIMO) systems with large-scale transmit antenna arrays, often called massive MIMO, are a very promising direction for 5G due to their ability to increase capacity and enhance both spectrum and energy efficiency. To get the benefit of massive MIMO systems, accurate downlink channel state information at the transmitter (CSIT) is essential for downlink beamforming and resource allocation. Conventional approaches to obtain CSIT for FDD massive MIMO systems require downlink training and CSI feedback. However, such training will cause a large overhead for massive MIMO systems because of the large dimensionality of the channel matrix. In this dissertation, we improve the performance of FDD massive MIMO networks in terms of downlink training overhead reduction, by designing an efficient downlink beamforming method and developing a new algorithm to estimate the channel state information based on compressive sensing techniques. First, we design an efficient downlink beamforming method based on partial CSI. By exploiting the relationship between uplink direction of arrivals (DoAs) and downlink direction of departures (DoDs), we derive an expression for estimated downlink DoDs, which will be used for downlink beamforming. Second, By exploiting the sparsity structure of downlink channel matrix, we develop an algorithm that selects the best features from the measurement matrix to obtain efficient CSIT acquisition that can reduce the downlink training overhead compared with conventional LS/MMSE estimators. In both cases, we compare the performance of our proposed beamforming method with traditional methods in terms of downlink achievable rate and simulation results show that our proposed method outperform the traditional beamforming methods.
Naresh Kumar Sampath Kumar
Complexity of Rules Sets in Mining Incomplete Data Using Characteristic Sets and Generalized Maximal Consistent BlocksWhen & Where:
2001 B Eaton Hall
Committee Members:
Jerzy Grzymala-Busse, ChairPrasad Kulkarni
Richard Wang
Abstract
The process of going through data to discover hidden connections and predict future trends has a long history. In this data-driven world, data mining is an important process to extract knowledge or insights from data in various forms. It explores the unknown credible patterns which are significant in solving many problems. There are quite a few techniques in data mining including classification, clustering, and prediction. We will discuss the classification, by using a technique called rule induction using four different approaches.
We compare the complexity of rule sets induced using characteristic sets and maximal consistent blocks. The complexity of rule sets is determined by the total number of rules induced for a given data set and the total number of conditions present in each rule. We used Incomplete Data sets to induce rules. These data sets have missing attribute values. Both methods were implemented and analyzed to check how it influences the complexity. Preliminary results suggest that the choice between characteristic sets and generalized maximal consistent blocks is inconsequential. But the cardinality of the rule sets is always smaller for incomplete data sets with “do not care” conditions. Thus, the choice between interpretations of the missing attribute value is more important than the choice between characteristic sets and generalized maximal consistent blocks.
Usman Sajid
ZiZoNet: A Zoom-In and Zoom-Out Mechanism for Crowd Counting in Static ImagesWhen & Where:
246 Nichols Hall
Committee Members:
Guanghui Wang, ChairBo Luo
Heechul Yun
Abstract
As people gather during different social, political or musical events, automated crowd analysis can lead to effective and better management of such events to prevent any unwanted scene as well as avoid political manipulation of crowd numbers. Crowd counting remains an integral part of crowd analysis and also an active research area in the field of computer vision. Existing methods fail to perform where crowd density is either too high or too low in an image, thus resulting in either overestimation or underestimation. These methods also mix crowd-like cluttered background regions (e.g. tree leaves or small and continuous patterns) in images with actual crowd, resulting in further crowd overestimation. In this work, we present a novel deep convolutional neural network (CNN) based framework ZiZoNet for automated crowd counting in static images in very low to very high crowd density scenarios to address above issues. ZiZoNet consists of three modules namely Crowd Density Classifier (CDC), Decision Module (DM) and Count Regressor Module (CRM). The test image, divided into 224x224 patches, passes through crowd density classifier (CDC) that classifies each patch to a class label (no-crowd (NC), low-crowd (LC), medium-crowd (MC), high-crowd (HC)). Based on the CDC information and using either heuristic Rule-set Engine (RSE) or machine learning based Random Forest based Decision Block (RFDB), DM decides which mode (zoom-in, normal or zoom-out) this image should use for crowd counting. CRM then performs patch-wise crowd estimate for this image accordingly as decided or instructed by the DM module. Extensive experiments on three diverse and challenging crowd counting benchmarks (UCF-QNRF, ShanghaiTech, AHU-Crowd) show that our method outperforms current state-of-the-art models under most of the evaluation criteria.
Ernesto Alexander Ramos
Tunable Surface Plasmon DynamicsWhen & Where:
2001 B Eaton Hall
Committee Members:
Alessandro Salandrino, ChairChristopher Allen
Rongqing Hui
Abstract
Due to their extreme spatial confinement, surface plasmon resonances show great potential in the design of future devices that would blur the boundaries between electronics and optics. Traditionally, plasmonic interactions are induced with geometries involving noble metals and dielectrics. However, accessing these plasmonic modes requires delicate election of material parameters with little margin for error, controllability, or room for signal bandwidth. To rectify this, two novel plasmonic mechanisms with a high degree of control are explored: For the near infrared region, transparent conductive oxides (TCOs) exhibit tunability not only in "static" plasmon generation (through material doping) but could also allow modulation on a plasmon carrier through external bias induced switching. These effects rely on the electron accumulation layer that is created at the interface between an insulator and a doped oxide. Here a rigorous study of the electromagnetic characteristics of these electron accumulation layers is presented. As a consequence of the spatially graded permittivity profiles of these systems it will be shown that these systems display unique properties. The concept of Accumulation-layer Surface Plasmons (ASP) is introduced and the conditions for the existence or for the suppression of surface-wave eigenmodes are analyzed. A second method could allow access to modes of arbitrarily high order. Sub-wavelength plasmonic nanoparticles can support an infinite discrete set of orthogonal localized surface plasmon modes, however only the lowest order resonances can be effectively excited by incident light alone. By allowing the background medium to vary in time, novel localized surface plasmon dynamics emerge. In particular, we show that these temporal permittivity variations lift the orthogonality of the localized surface plasmon modes and introduce coupling among different angular momentum states. Exploiting these dynamics, surface plasmon amplification of high order resonances can be achieved under the action of a spatially uniform optical pump of appropriate frequency.