I2S Masters/ Doctoral Theses


All students and faculty are welcome to attend the final defense of I2S 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

Elizabeth Wyss

A New Frontier for Software Security: Diving Deep into npm

When & Where:


Eaton Hall, Room 2001B

Degree Type:

PhD Dissertation Defense

Committee Members:

Drew Davidson, Chair
Alex Bardas
Fengjun Li
Bo Luo
J. Walker

Abstract

Open-source package managers (e.g., npm for Node.js) have become an established component of modern software development. Rather than creating applications from scratch, developers may employ modular software dependencies and frameworks--called packages--to serve as building blocks for writing larger applications. Package managers make this process easy. With a simple command line directive, developers are able to quickly fetch and install packages across vast open-source repositories. npm--the largest of such repositories--alone hosts millions of unique packages and serves billions of package downloads each week. 

However, the widespread code sharing resulting from open-source package managers also presents novel security implications. Vulnerable or malicious code hiding deep within package dependency trees can be leveraged downstream to attack both software developers and the end-users of their applications. This downstream flow of software dependencies--dubbed the software supply chain--is critical to secure.

This research provides a deep dive into the npm-centric software supply chain, exploring distinctive phenomena that impact its overall security and usability. Such factors include (i) hidden code clones--which may stealthily propagate known vulnerabilities, (ii) install-time attacks enabled by unmediated installation scripts, (iii) hard-coded URLs residing in package code, (iv) the impacts of open-source development practices, (v) package compromise via malicious updates, (vi) spammers disseminating phishing links within package metadata, and (vii) abuse of cryptocurrency protocols designed to reward the creators of high-impact packages. For each facet, tooling is presented to identify and/or mitigate potential security impacts. Ultimately, it is our hope that this research fosters greater awareness, deeper understanding, and further efforts to forge a new frontier for the security of modern software supply chains. 


Alfred Fontes

Optimization and Trade-Space Analysis of Pulsed Radar-Communication Waveforms using Constant Envelope Modulations

When & Where:


Nichols Hall, Room 246 (Executive Conference Room)

Degree Type:

MS Thesis Defense

Committee Members:

Patrick McCormick, Chair
Shannon Blunt
Jonathan Owen


Abstract

Dual function radar communications (DFRC) is a method of co-designing a single radio frequency system to perform simultaneous radar and communications service. DFRC is ultimately a compromise between radar sensing performance and communications data throughput due to the conflicting requirements between the sensing and information-bearing signals.

A novel waveform-based DFRC approach is phase attached radar communications (PARC), where a communications signal is embedded onto a radar pulse via the phase modulation between the two signals. The PARC framework is used here in a new waveform design technique that designs the radar component of a PARC signal to match the PARC DFRC waveform expected power spectral density (PSD) to a desired spectral template. This provides better control over the PARC signal spectrum, which mitigates the issue of PARC radar performance degradation from spectral growth due to the communications signal. 

The characteristics of optimized PARC waveforms are then analyzed to establish a trade-space between radar and communications performance within a PARC DFRC scenario. This is done by sampling the DFRC trade-space continuum with waveforms that contain a varying degree of communications bandwidth, from a pure radar waveform (no embedded communications) to a pure communications waveform (no radar component). Radar performance, which is degraded by range sidelobe modulation (RSM) from the communications signal randomness, is measured from the PARC signal variance across pulses; data throughput is established as the communications performance metric. Comparing the values of these two measures as a function of communications symbol rate explores the trade-offs in performance between radar and communications with optimized PARC waveforms.


Arin Dutta

Performance Analysis of Distributed Raman Amplification with Different Pumping Configurations

When & Where:


Nichols Hall, Room 246 (Executive Conference Room)

Degree Type:

PhD Dissertation Defense

Committee Members:

Rongqing Hui, Chair
Morteza Hashemi
Rachel Jarvis
Alessandro Saladrino
Hui Zhao

Abstract

As internet services like high-definition videos, cloud computing, and artificial intelligence keep growing, optical networks need to keep up with the demand for more capacity. Optical amplifiers play a crucial role in offsetting fiber loss and enabling long-distance wavelength division multiplexing (WDM) transmission in high-capacity systems. Various methods have been proposed to enhance the capacity and reach of fiber communication systems, including advanced modulation formats, dense wavelength division multiplexing (DWDM) over ultra-wide bands, space-division multiplexing, and high-performance digital signal processing (DSP) technologies. To maintain higher data rates along with maximizing the spectral efficiency of multi-level modulated signals, a higher Optical Signal-to-Noise Ratio (OSNR) is necessary. Despite advancements in coherent optical communication systems, the spectral efficiency of multi-level modulated signals is ultimately constrained by fiber nonlinearity. Raman amplification is an attractive solution for wide-band amplification with low noise figures in multi-band systems.

Distributed Raman Amplification (DRA) have been deployed in recent high-capacity transmission experiments to achieve a relatively flat signal power distribution along the optical path and offers the unique advantage of using conventional low-loss silica fibers as the gain medium, effectively transforming passive optical fibers into active or amplifying waveguides. Also, DRA provides gain at any wavelength by selecting the appropriate pump wavelength, enabling operation in signal bands outside the Erbium doped fiber amplifier (EDFA) bands. Forward (FW) Raman pumping configuration in DRA can be adopted to further improve the DRA performance as it is more efficient in OSNR improvement because the optical noise is generated near the beginning of the fiber span and attenuated along the fiber. Dual-order FW pumping scheme helps to reduce the non-linear effect of the optical signal and improves OSNR by more uniformly distributing the Raman gain along the transmission span.

The major concern with Forward Distributed Raman Amplification (FW DRA) is the fluctuation in pump power, known as relative intensity noise (RIN), which transfers from the pump laser to both the intensity and phase of the transmitted optical signal as they propagate in the same direction. Additionally, another concern of FW DRA is the rise in signal optical power near the start of the fiber span, leading to an increase in the non-linear phase shift of the signal. These factors, including RIN transfer-induced noise and non-linear noise, contribute to the degradation of system performance in FW DRA systems at the receiver.

As the performance of DRA with backward pumping is well understood with relatively low impact of RIN transfer, our research  is focused on the FW pumping configuration, and is intended to provide a comprehensive analysis on the system performance impact of dual order FW Raman pumping, including signal intensity and phase noise induced by the RINs of both 1st and the 2nd order pump lasers, as well as the impacts of linear and nonlinear noise. The efficiencies of pump RIN to signal intensity and phase noise transfer are theoretically analyzed and experimentally verified by applying a shallow intensity modulation to the pump laser to mimic the RIN. The results indicate that the efficiency of the 2nd order pump RIN to signal phase noise transfer can be more than 2 orders of magnitude higher than that from the 1st order pump. Then the performance of the dual order FW Raman configurations is compared with that of single order Raman pumping to understand trade-offs of system parameters. The nonlinear interference (NLI) noise is analyzed to study the overall OSNR improvement when employing a 2nd order Raman pump. Finally, a DWDM system with 16-QAM modulation is used as an example to investigate the benefit of DRA with dual order Raman pumping and with different pump RIN levels. We also consider a DRA system using a 1st order incoherent pump together with a 2nd order coherent pump. Although dual order FW pumping corresponds to a slight increase of linear amplified spontaneous emission (ASE) compared to using only a 1st order pump, its major advantage comes from the reduction of nonlinear interference noise in a DWDM system. Because the RIN of the 2nd order pump has much higher impact than that of the 1st order pump, there should be more stringent requirement on the RIN of the 2nd order pump laser when dual order FW pumping scheme is used for DRA for efficient fiber-optic communication. Also, the result of system performance analysis reveals that higher baud rate systems, like those operating at 100Gbaud, are less affected by pump laser RIN due to the low-pass characteristics of the transfer of pump RIN to signal phase noise.


Rich Simeon

Delay-Doppler Channel Estimation for High-Speed Aeronautical Mobile Telemetry Applications

When & Where:


Eaton Hall, Room 2001B

Degree Type:

PhD Comprehensive Defense

Committee Members:

Erik Perrins, Chair
Shannon Blunt
Morteza Hashemi
James Stiles
Craig McLaughlin

Abstract

The next generation of digital communications systems aims to operate in high-Doppler environments such as high-speed trains and non-terrestrial networks that utilize satellites in low-Earth orbit. Current generation systems use Orthogonal Frequency Division Multiplexing modulation which is known to suffer from inter-carrier interference (ICI) when different channel paths have dissimilar Doppler shifts. 

A new Orthogonal Time Frequency Space (OTFS) modulation (also known as Delay-Doppler modulation) is proposed as a candidate modulation for 6G networks that is resilient to ICI. To date, OTFS demodulation designs have focused on the use cases of popular urban terrestrial channel models where path delay spread is a fraction of the OTFS symbol duration. However, wireless wide-area networks that operate in the aeronautical mobile telemetry (AMT) space can have large path delay spreads due to reflections from distant geographic features. This presents problems for existing channel estimation techniques which assume a small maximum expected channel delay, since data transmission is paused to sound the channel by an amount equal to twice the maximum channel delay. The dropout in data contributes to a reduction in spectral efficiency.

Our research addresses OTFS limitations in the AMT use case. We start with an exemplary OTFS framework with parameters optimized for AMT. Following system design, we focus on two distinct areas to improve OTFS performance in the AMT environment. First we propose a new channel estimation technique using a pilot signal superimposed over data that can measure large delay spread channels with no penalty in spectral efficiency. A successive interference cancellation algorithm is used to iteratively improve channel estimates and jointly decode data. A second aspect of our research aims to equalize in delay-Doppler space. In the delay-Doppler paradigm, the rapid channel variations seen in the time-frequency domain is transformed into a sparse quasi-stationary channel in the delay-Doppler domain. We propose to use machine learning using Gaussian Process Regression to take advantage of the sparse and stationary channel and learn the channel parameters to compensate for the effects of fractional Doppler in which simpler channel estimation techniques cannot mitigate. Both areas of research can advance the robustness of OTFS across all communications systems.


Mohammad Ful Hossain Seikh

AAFIYA: Antenna Analysis in Frequency-domain for Impedance and Yield Assessment

When & Where:


Eaton Hall, Room 2001B

Degree Type:

MS Project Defense

Committee Members:

Jim Stiles, Chair
Rachel Jarvis
Alessandro Salandrino


Abstract

This project presents AAFIYA (Antenna Analysis in Frequency-domain for Impedance and Yield Assessment), a modular Python toolkit developed to automate and streamline the characterization and analysis of radiofrequency (RF) antennas using both measurement and simulation data. Motivated by the need for reproducible, flexible, and publication-ready workflows in modern antenna research, AAFIYA provides comprehensive support for all major antenna metrics, including S-parameters, impedance, gain and beam patterns, polarization purity, and calibration-based yield estimation. The toolkit features robust data ingestion from standard formats (such as Touchstone files and beam pattern text files), vectorized computation of RF metrics, and high-quality plotting utilities suitable for scientific publication.

Validation was carried out using measurements from industry-standard electromagnetic anechoic chamber setups involving both Log Periodic Dipole Array (LPDA) reference antennas and Askaryan Radio Array (ARA) Bottom Vertically Polarized (BVPol) antennas, covering a frequency range of 50–1500 MHz. Key performance metrics, such as broadband impedance matching, S11 and S21 related calculations, 3D realized gain patterns, vector effective lengths,  and cross-polarization ratio, were extracted and compared against full-wave electromagnetic simulations (using HFSS and WIPL-D). The results demonstrate close agreement between measurement and simulation, confirming the reliability of the workflow and calibration methodology.

AAFIYA’s open-source, extensible design enables rapid adaptation to new experiments and provides a foundation for future integration with machine learning and evolutionary optimization algorithms. This work not only delivers a validated toolkit for antenna research and pedagogy but also sets the stage for next-generation approaches in automated antenna design, optimization, and performance analysis.


Soumya Baddham

Battling Toxicity: A Comparative Analysis of Machine Learning Models for Content Moderation

When & Where:


Eaton Hall, Room 2001B

Degree Type:

MS Project Defense

Committee Members:

David Johnson, Chair
Prasad Kulkarni
Hongyang Sun


Abstract

With the exponential growth of user-generated content, online platforms face unprecedented challenges in moderating toxic and harmful comments. Due to this, Automated content moderation has emerged as a critical application of machine learning, enabling platforms to ensure user safety and maintain community standards. Despite its importance, challenges such as severe class imbalance, contextual ambiguity, and the diverse nature of toxic language often compromise moderation accuracy, leading to biased classification performance.

This project presents a comparative analysis of machine learning approaches for a Multi-Label Toxic Comment Classification System using the Toxic Comment Classification dataset from Kaggle.  The study examines the performance of traditional algorithms, such as Logistic Regression, Random Forest, and XGBoost, alongside deep architectures, including Bi-LSTM, CNN-Bi-LSTM, and DistilBERT. The proposed approach utilizes word-level embeddings across all models and examines the effects of architectural enhancements, hyperparameter optimization, and advanced training strategies on model robustness and predictive accuracy.

The study emphasizes the significance of loss function optimization and threshold adjustment strategies in improving the detection of minority classes. The comparative results reveal distinct performance trade-offs across model architectures, with transformer models achieving superior contextual understanding at the cost of computational complexity. At the same time, deep learning approaches(LSTM models) offer efficiency advantages. These findings establish evidence-based guidelines for model selection in real-world content moderation systems, striking a balance between accuracy requirements and operational constraints.


Past Defense Notices

Dates

Manu Chaudhary

Utilizing Quantum Computing for Solving Multidimensional Partial Differential Equations

When & Where:


Eaton Hall, Room 2001B

Degree Type:

PhD Dissertation Defense

Committee Members:

Esam El-Araby, Chair
Perry Alexander
Tamzidul Hoque
Prasad Kulkarni
Tyrone Duncan

Abstract

Quantum computing has the potential to revolutionize computational problem-solving by leveraging the quantum mechanical phenomena of superposition and entanglement, which allows for processing a large amount of information simultaneously. This capability is significant in the numerical solution of complex and/or multidimensional partial differential equations (PDEs), which are fundamental to modeling various physical phenomena. There are currently many quantum techniques available for solving partial differential equations (PDEs), which are mainly based on variational quantum circuits. However, the existing quantum PDE solvers, particularly those based on variational quantum eigensolver (VQE) techniques, suffer from several limitations. These include low accuracy, high execution times, and low scalability on quantum simulators as well as on noisy intermediate-scale quantum (NISQ) devices, especially for multidimensional PDEs.

In this work, we propose an efficient and scalable algorithm for solving multidimensional PDEs. We present two variants of our algorithm: the first leverages finite-difference method (FDM), classical-to-quantum (C2Q) encoding, and numerical instantiation, while the second employs FDM, C2Q, and column-by-column decomposition (CCD). Both variants are designed to enhance accuracy and scalability while reducing execution times. We have validated and evaluated our proposed concepts using a number of case studies including multidimensional Poisson equation, multidimensional heat equation, Black Scholes equation, and Navier-Stokes equation for computational fluid dynamics (CFD) achieving promising results. Our results demonstrate higher accuracy, higher scalability, and faster execution times compared to VQE-based solvers on noise-free and noisy quantum simulators from IBM. Additionally, we validated our approach on hardware emulators and actual quantum hardware, employing noise mitigation techniques. This work establishes a practical and effective approach for solving PDEs using quantum computing for engineering and scientific applications.


Alex Manley

Taming Complexity in Computer Architecture through Modern AI-Assisted Design and Education

When & Where:


Nichols Hall, Room 250 (Gemini Room)

Degree Type:

MS Thesis Defense

Committee Members:

Heechul Yun, Chair
Tamzidul Hoque
Mohammad Alian
Prasad Kulkarni

Abstract

The escalating complexity inherent in modern computer architecture presents significant challenges for both professional hardware designers and students striving to gain foundational understanding. Historically, the steady improvement of computer systems was driven by transistor scaling, predictable performance increases, and relatively straightforward architectural paradigms. However, with the end of traditional scaling laws and the rise of heterogeneous and parallel architectures, designers now face unprecedented intricacies involving power management, thermal constraints, security considerations, and sophisticated software interactions. Prior tools and methodologies, often reliant on complex, command-line driven simulations, exacerbate these challenges by introducing steep learning curves, creating a critical need for more intuitive, accessible, and efficient solutions. To address these challenges, this thesis introduces two innovative, modern tools. 

The first tool, SimScholar, provides an intuitive graphical user interface (GUI) built upon the widely-used gem5 simulator. SimScholar significantly simplifies the simulation process, enabling students and educators to more effectively engage with architectural concepts through a visually guided environment, both reducing complexity and enhancing conceptual understanding. Supporting SimScholar, the gem5 Extended Modules API (gEMA) offers streamlined backend integration with gem5, ensuring efficient communication, modularity, and maintainability.

The second contribution, gem5 Co-Pilot, delivers an advanced framework for architectural design space exploration (DSE). Co-Pilot integrates cycle-accurate simulation via gem5, detailed power and area modeling through McPAT, and intelligent optimization assisted by a large language model (LLM). Central to Co-Pilot is the Design Space Declarative Language (DSDL), a Python-based domain-specific language that facilitates structured, clear specification of design parameters and constraints.

Collectively, these tools constitute a comprehensive approach to taming complexity in computer architecture, offering powerful, user-friendly solutions tailored to both educational and professional settings.


Prashanthi Mallojula

On the Security of Mobile and Auto Companion Apps

When & Where:


Eaton Hall, Room 2001B

Degree Type:

PhD Dissertation Defense

Committee Members:

Bo Luo, Chair
Alex Bardas
Fengjun Li
Hongyang Sun
Huazhen Fang

Abstract

The rapid development of mobile apps on modern smartphone platforms has raised critical concerns regarding user data privacy and the security of app-to-device communications, particularly with companion apps that interface with external IoT or cyber-physical systems (CPS). In this dissertation, we investigate two major aspects of mobile app security: the misuse of permission mechanisms and the security of app to device communication in automotive companion apps.

 

Mobile apps seek user consent for accessing sensitive information such as location and personal data. However, users often blindly accept these permission requests, allowing apps to abuse this mechanism. As long as a permission is requested, state-of-the-art security mechanisms typically treat it as legitimate. This raises a critical question: Are these permission requests always valid? To explore this, we validate permission requests using statistical analysis on permission sets extracted from groups of functionally similar apps. We identify mobile apps with abusive permission access and quantify the risk of information leakage posed by each app. Through a large-scale statistical analysis of permission sets from over 200,000 Android apps, our findings reveal that approximately 10% of the apps exhibit highly risky permission usage. 

 

Next, we present a comprehensive study of automotive companion apps, a rapidly growing yet underexplored category of mobile apps. These apps are used for vehicle diagnostics, telemetry, and remote control, and they often interface with in-vehicle networks via OBD-II dongles, exposing users to significant privacy and security risks. Using a hybrid methodology that combines static code analysis, dynamic runtime inspection, and network traffic monitoring, we analyze 154 publicly available Android automotive apps. Our findings uncover a broad range of critical vulnerabilities. Over 74% of the analyzed apps exhibit vulnerabilities that could lead to private information leakage, property theft, or even real-time safety risks while driving. Specifically, 18 apps were found to connect to open OBD-II dongles without requiring any authentication, accept arbitrary CAN bus commands from potentially malicious users, and transmit those commands to the vehicle without validation. 16 apps were found to store driving logs in external storage, enabling attackers to reconstruct trip histories and driving patterns. We demonstrate several real-world attack scenarios that illustrate how insecure data storage and communication practices can compromise user privacy and vehicular safety. Finally, we discuss mitigation strategies and detail the responsible disclosure process undertaken with the affected developers.


Syed Abid Sahdman

Soliton Generation and Pulse Optimization using Nonlinear Transmission Lines

When & Where:


Eaton Hall, Room 2001B

Degree Type:

MS Thesis Defense

Committee Members:

Alessandro Salandrino, Chair
Shima Fardad
Morteza Hashemi


Abstract

Nonlinear Transmission Lines (NLTLs) have gained significant interest due to their ability to generate ultra-short, high-power RF pulses, which are valuable in applications such as ultrawideband radar, space vehicles, and battlefield communication disruption. The waveforms generated by NLTLs offer frequency diversity not typically observed in High-Power Microwave (HPM) sources based on electron beams. Nonlinearity in lumped element transmission lines is usually introduced using voltage-dependent capacitors due to their simplicity and widespread availability. The periodic structure of these lines introduces dispersion, which broadens pulses. In contrast, nonlinearity causes higher-amplitude regions to propagate faster. The interaction of these effects results in the formation of stable, self-localized waveforms known as solitons.

Soliton propagation in NLTLs can be described by the Korteweg-de Vries (KdV) equation. In this thesis, the Bäcklund Transformation (BT) method has been used to derive both single and two-soliton solutions of the KdV equation. This method links two different partial differential equations (PDEs) and their solutions to produce solutions for nonlinear PDEs. The two-soliton solution is obtained from the single soliton solution using a nonlinear superposition principle known as Bianchi’s Permutability Theorem (BPT). Although the KdV model is suitable for NLTLs where the capacitance-voltage relationship follows that of a reverse-biased p-n junction, it cannot generally represent arbitrary nonlinear capacitance characteristics.

To address this limitation, a Finite Difference Time Domain (FDTD) method has been developed to numerically solve the NLTL equation for soliton propagation. To demonstrate the pulse sharpening and RF generation capability of a varactor-loaded NLTL, a 12-section lumped element circuit has been designed and simulated using LTspice and verified with the calculated result. In airborne radar systems, operational constraints such as range, accuracy, data rate, environment, and target type require flexible waveform design, including variation in pulse widths and pulse repetition frequencies. A gradient descent optimization technique has been employed to generate pulses with varying amplitudes and frequencies by optimizing the NLTL parameters. This work provides a theoretical analysis and numerical simulation to study soliton propagation in NLTLs and demonstrates the generation of tunable RF pulses through optimized circuit design.


Vinay Kumar Reddy Budideti

NutriBot: An AI-Powered Personalized Nutrition Recommendation Chatbot Using Rasa

When & Where:


Eaton Hall, Room 2001B

Degree Type:

MS Project Defense

Committee Members:

David Johnson, Chair
Victor Frost
Prasad Kulkarni


Abstract

In recent years, the intersection of Artificial Intelligence and healthcare has paved the way for intelligent dietary assistance. NutriBot is an AI-powered chatbot developed using the Rasa framework to deliver personalized nutrition recommendations based on user preferences, diet types, and nutritional goals. This full-stack system integrates Rasa NLU, a Flask backend, the Nutritionix API for real-time food data, and a React.js + Tailwind CSS frontend for seamless interaction. The system is containerized using Docker and deployable on cloud platforms like GCP. 

The chatbot supports multi-turn conversations, slot-filling, and remembers user preferences such as dietary restrictions or nutrient focus (e.g., high protein). Evaluation of the system showed perfect intent and entity recognition accuracy, fast API response times, and user-friendly fallback handling. While NutriBot currently lacks persistent user profiles and multilingual support, it offers a highly accurate, scalable framework for future extensions such as fitness tracker integration, multilingual capabilities, and smart assistant deployment.


Arun Kumar Punjala

Deep Learning-Based MRI Brain Tumor Classification: Evaluating Sequential Architectures for Diagnostic Accuracy

When & Where:


Eaton Hall, Room 2001B

Degree Type:

MS Project Defense

Committee Members:

David Johnson, Chair
Prasad Kulkarni
Dongjie Wang


Abstract

Accurate classification of brain tumors from MRI scans plays a vital role in assisting clinical diagnosis and treatment planning. This project investigates and compares three deep learning-based classification approaches designed to evaluate the effectiveness of integrating recurrent layers into conventional convolutional architectures. Specifically, a CNN-LSTM model, a CNN-RNN model with GRU units, and a baseline CNN classifier using EfficientNetB0 are developed and assessed on a curated MRI dataset.

The CNN-LSTM model uses ResNet50 as a feature extractor, with spatial features reshaped and passed through stacked LSTM layers to explore sequential learning on static medical images. The CNN-RNN model implements TimeDistributed convolutional layers followed by GRUs, examining the potential benefits of GRU-based modeling. The EfficientNetB0-based CNN model, trained end-to-end without recurrent components, serves as the performance baseline.

All three models are evaluated using training accuracy, validation loss, confusion matrices, and class-wise performance metrics. Results show that the CNN-LSTM architecture provides the most balanced performance across tumor types, while the CNN-RNN model suffers from mild overfitting. The EfficientNetB0 baseline offers stable and efficient classification for general benchmarking.


Ganesh Nurukurti

Customer Behavior Analytics and Recommendation System for E-Commerce

When & Where:


Eaton Hall, Room 2001B

Degree Type:

MS Project Defense

Committee Members:

David Johnson, Chair
Prasad 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.


Mahmudul Hasan

Assertion-Based Security Assessment of Hardware IP Protection Methods

When & Where:


Eaton Hall, Room 2001B

Degree Type:

MS Thesis Defense

Committee Members:

Tamzidul Hoque, Chair
Esam 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 Bands

When & Where:


Nichols Hall, Room 246 (Executive Conference Room)

Degree Type:

MS Thesis Defense

Committee Members:

Morteza Hashemi, Chair
Victor 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.


Srijanya Chetikaneni

Plant Disease Prediction Using Transfer Learning

When & Where:


Eaton Hall, Room 2001B

Degree Type:

MS Project Defense

Committee Members:

David Johnson, Chair
Prasad 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.