Electrical & Computer Engineering Department Theses and Dissertations

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https://hdl.handle.net/1805/2087

Information about the Purdue School of Engineering and Technology Graduate Degree Programs available at IUPUI can be found at: http://www.engr.iupui.edu/academics.shtml

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Trustworthy and Efficient Blockchain-based E-commerce Model
(2024-08) Shankar Kumar, Valli Sanghami; Lee, John; King, Brian; Kim, Dongsoo; Hu, Qin
Amidst the rising popularity of digital marketplaces, addressing issues such as non- payment/non-delivery crimes, centralization risks, hacking threats, and the complexity of ownership transfers has become imperative. Many existing studies exploring blockchain technology in digital marketplaces and asset management merely touch upon various application scenarios without establishing a unified platform that ensures trustworthiness and efficiency across the product life cycle. In this thesis, we focus on designing a reliable and efficient e-commerce model to trade various assets. To enhance customer engagement through consensus, we utilize the XGBoost algorithm to identify loyal nodes from the platform entities pool. Alongside appointed nodes, these loyal nodes actively participate in the consensus process. The consensus algorithm guarantees that all involved nodes reach an agreement on the blockchain’s current state. We introduce a novel consensus mechanism named Modified- Practical Byzantine Fault Tolerance (M-PBFT), derived from the Practical Byzantine Fault Tolerance (PBFT) protocol to minimize communication overhead and improve overall efficiency. The modifications primarily target the leader election process and the communication protocols between leader and follower nodes within the PBFT consensus framework. In the domain of tangible assets, our primary objective is to elevate trust among various stakeholders and bolster the reputation of sellers. As a result, we aim to validate secondhand products and their descriptions provided by the sellers before the secondhand products are exchanged. This validation process also holds various entities accountable for their actions. We employ validators based on their location and qualifications to validate the products’ descriptions and generate validation certificates for the products, which are then securely recorded on the blockchain. To incentivize the participation of validator nodes and up- hold honest validation of product quality, we introduce an incentive mechanism leveraging Stackelberg game theory. On the other hand, for optimizing intangible assets management, we employ Non-Fungible Tokens (NFT) technology to tokenize these assets. This approach enhances traceability of ownership, transactions, and historical data, while also automating processes like dividend distributions, royalty payments, and ownership transfers through smart contracts. Initially, sellers mint NFTs and utilize the InterPlanetary File System (IPFS) to store the files related to NFTs, NFT metadata, or both since IPFS provides resilience and decentralized storage solutions to our network. The data stored in IPFS is encrypted for security purposes. Further, to aid sellers in pricing their NFTs efficiently, we employ the Stackelberg mechanism. Furthermore, to achieve finer access control in NFTs containing sensitive data and increase sellers’ profits, we propose a Popularity-based Adaptive NFT Management Scheme (PANMS) utilizing Reinforcement Learning (RL). To facilitate prompt and effective asset sales, we design a smart contract-powered auction mechanism. Also, to enhance data recording and event response efficiency, we introduce a weighted L-H index algorithm and transaction prioritization features in the network. The weighted L-H index algorithm determines efficient nodes to broadcast transactions. Transaction prioritization prioritizes certain transactions such as payments, verdicts during conflicts between sellers and validators, and validation reports to improve the efficiency of the platform. Simulation experiments are conducted to demonstrate the accuracy and efficiency of our proposed schemes.
Geocasting-based Traffic Management Message Delivery Using C-V2X
(2024-08) Mathew, Abin; Chen, Yaobin; Li, Feng; King, Brian
Cellular-Vehicle to Everything or C-V2X refers to vehicles connected to their surroundings using cellular based networks. With the rise of connected vehicles, C-V2X is emerging as one of the major standards for message transmission in automotive scenarios. The project aims to study the feasibility of C-V2X-based message transmission by building a prototype system, named RampCast, for transmitting traffic information from roadside message boards to vehicles. The RampCast framework would also implement geocasting-based algorithms to deliver messages to targeted vehicles. These algorithms focus on improving location-based message delivery using retransmission and prioritization strategies. The messages used for transmission are selected from the 511 web application built by INDOT, which contains the live traffic information for the state of Indiana which includes Travel Time information, Crash Alerts, Construction Alerts etc. The major objectives of this project consist of building the RampCast prototype, a system implementing C-V2X networks using a Software Defined Radio(SDR). The RampCast system implements a Publisher-subscriber messaging architecture with the primary actors being a Road Side Unit(RSU) and a Vehicle Onboard Unit(OBU). A data store containing traffic messages sourced from the 511 API is set up to be the input to the RampCast system. An end-to-end message transmission pipeline is built that would implement message transmission algorithms on the RSU and OBU side. Finally, the performance of message transmission on the RampCast system is evaluated using a metrics-capturing module. The system was evaluated on a test track in Columbus, Indiana. The performance metrics of the system were captured and analyzed, and the system met the key performance indicators for Latency, Packet Delivery Rate, and Packet Inter-reception Rate. The results indicate the satisfactory performance of the C-V2X standard for message transmission in the RampCast traffic guidance scenarios.
Explainable AI Methods For Enhancing AI-Based Network Intrusion Detection Systems
(2024-08) Arreche, Osvaldo Guilherme; King, Brian S.; Abdallah, Mustafa; El-Sharkawy, Mohamed A.
In network security, the exponential growth of intrusions stimulates research toward developing advanced artificial intelligence (AI) techniques for intrusion detection systems (IDS). However, the reliance on AI for IDS presents challenges, including the performance variability of different AI models and the lack of explainability of their decisions, hindering the comprehension of outputs by human security analysts. Hence, this thesis proposes end-to-end explainable AI (XAI) frameworks tailored to enhance the understandability and performance of AI models in this context.The first chapter benchmarks seven black-box AI models across one real-world and two benchmark network intrusion datasets, laying the foundation for subsequent analyses. Subsequent chapters delve into feature selection methods, recognizing their crucial role in enhancing IDS performance by extracting the most significant features for identifying anomalies in network security. Leveraging XAI techniques, novel feature selection methods are proposed, showcasing superior performance compared to traditional approaches.Also, this thesis introduces an in-depth evaluation framework for black-box XAI-IDS, encompassing global and local scopes. Six evaluation metrics are analyzed, including descrip tive accuracy, sparsity, stability, efficiency, robustness, and completeness, providing insights into the limitations and strengths of current XAI methods.Finally, the thesis addresses the potential of ensemble learning techniques in improving AI-based network intrusion detection by proposing a two-level ensemble learning framework comprising base learners and ensemble methods trained on input datasets to generate evalua tion metrics and new datasets for subsequent analysis. Feature selection is integrated into both levels, leveraging XAI-based and Information Gain-based techniques.Holistically, this thesis offers a comprehensive approach to enhancing network intrusion detection through the synergy of AI, XAI, and ensemble learning techniques by providing open-source codes and insights into model performances. Therefore, it contributes to the security advancement of interpretable AI models for network security, empowering security analysts to make informed decisions in safeguarding networked systems.
Large Language Models for Unsupervised Keyphrase Extraction and Biomedical Data Analytics
(2024-08) Ding, Haoran; Luo, Xiao; King, Brian; Zhang, Qingxue; Li, Lingxi
Natural Language Processing (NLP), a vital branch of artificial intelligence, is designed to equip computers with the ability to comprehend and manipulate human language, facilitating the extraction and utilization of textual data. NLP plays a crucial role in harnessing the vast quantities of textual data generated daily, facilitating meaningful information extraction. Among the various techniques, keyphrase extraction stands out due to its ability to distill concise information from extensive texts, making it invaluable for summarizing and navigating content efficiently. The process of keyphrase extraction usually begins by generating candidates first and then ranking them to identify the most relevant phrases. Keyphrase extraction can be categorized into supervised and unsupervised approaches. Supervised methods typically achieve higher accuracy as they are trained on labeled data, which allows them to effectively capture and utilize patterns recognized during training. However, the dependency on extensive, well-annotated datasets limits their applicability in scenarios where such data is scarce or costly to obtain. On the other hand, unsupervised methods, while free from the constraints of labeled data, face challenges in capturing deep semantic relationships within text, which can impact their effectiveness. Despite these challenges, unsupervised keyphrase extraction holds significant promise due to its scalability and lower barriers to entry, as it does not require labeled datasets. This approach is increasingly favored for its potential to aid in building extensive knowledge bases from unstructured data, which can be particularly useful in domains where acquiring labeled data is impractical. As a result, unsupervised keyphrase extraction is not only a valuable tool for information retrieval but also a pivotal technology for the ongoing expansion of knowledge-driven applications in NLP. In this dissertation, we introduce three innovative unsupervised keyphrase extraction methods: AttentionRank, AGRank, and LLMRank. Additionally, we present a method for constructing knowledge graphs from unsupervised keyphrase extraction, leveraging the self-attention mechanism. The first study discusses the AttentionRank model, which utilizes a pre-trained language model to derive underlying importance rankings of candidate phrases through self-attention. This model employs a cross-attention mechanism to assess the semantic relevance between each candidate phrase and the document, enhancing the phrase ranking process. AGRank, detailed in the second study, is a sophisticated graph-based framework that merges deep learning techniques with graph theory. It constructs a candidate phrase graph using mutual attentions from a pre-trained language model. Both global document information and local phrase details are incorporated as enhanced nodes within the graph, and a graph algorithm is applied to rank the candidate phrases. The third study, LLMRank, leverages the strengths of large language models (LLMs) and graph algorithms. It employs LLMs to generate keyphrase candidates and then integrates global information through the text's graphical structures. This process reranks the candidates, significantly improving keyphrase extraction performance. The fourth study explores how self-attention mechanisms can be used to extract keyphrases from medical literature and generate query-related phrase graphs, improving text retrieval visualization. The mutual attentions of medical entities, extracted using a pre-trained model, form the basis of the knowledge graph. This, coupled with a specialized retrieval algorithm, allows for the visualization of long-range connections between medical entities while simultaneously displaying the supporting literature. In summary, our exploration of unsupervised keyphrase extraction and biomedical data analysis introduces novel methods and insights in NLP, particularly in information extraction. These contributions are crucial for the efficient processing of large text datasets and suggest avenues for future research and applications.
Deep Multimodal Physiological Learning of Cerebral Vasoregulation Dynamics on Stroke Patients Towards Precision Brain Medicine
(2024-08) Tipparti, Akanksha; Zhang, Qingxue; King, Brain; Yung-Ping Chien, Stanley
Impaired cerebral vasoregulation is one of the most common post-ischemic stroke effects. Diagnosis and prevention of this condition is often invasive, costly and in-effective. This impairment restricts the cerebral blood vessels to properly regulate blood flow, which is very important for normal brain functioning. Developing accurate, non-invasive and efficient methods to detect this condition aids in better stroke diagnosis and prevention. The aim of this thesis is to develop deep learning techniques for the purpose of detection of cerebral vasoregulation impairments by analyzing physiological signals. This research employs various Deep learning techniques like Convolution Neural Networks (CNN), Mo bileNet, and Long-Short-Term Memory (LSTM) to determine variety of physiological signals from the PhysioNet database like Electrocardio-gram (ECG), Transcranial Doppler (TCD), Electromyogram (EMG), and Blood Pressure(BP) as stroke or non-stroke subjects. The effectiveness of these algorithms is demonstrated by a classification accuracy of 90% for the combination of ECG and EMG signals. Furthermore, this research explores the importance of analyzing dynamic physiologi cal activities in determining the impairment. The dynamic activities include Sit-stand, Sit-stand-balance, Head-up-tilt, and Walk dataset from the PhysioNet website. CNN and MobileNetV3 are employed in classification purposes of these signals, attempting to iden tify cerebral health. The accuracy of the model and robustness of these methods is greatly enhanced when multiple signals are integrated. Overall, this study highlights the potential of deep multimodal physiological learning in the development of precision brain medicine further enhancing stroke diagnosis. The results pave the way for the development of advanced diagnostic tools to determine cerebral health.
Silicon Based Nano-electronic Synaptic Device for Neuromorphic Hardware
(2024-08) Sikder, Orthi; Schubert, Peter; King, Brian; Rizkalla, Maher; Agarwal, Mangilal
Porous silicon (po-Si) is a unique form of silicon (Si) that features tunable nanopores distributed throughout its bulk structure. While crystalline Si (c-Si) already boasts technological advantages, po-Si offers an additional key aspect with its large surface area relative to its small volume, making it highly conducive to surface chemistry. In this research, our focus centers on the design of a synaptic device based on po-Si, exploring its potential for neuromorphic hardware applications. To begin, we delve into the analysis of several electrical properties of po-Si using density functional theory (ab initio/first principles) calculations. Notably, we discover the presence of intra-pore dangling states within the bandgap region of po-Si. Although po-Si is known for its higher bandgap compared to c-Si, resulting in low carrier density and increased resistance, the existence of these dangling states significantly impacts its electronic transport. Additionally, we investigate the electric field driven modulation of dangling bonds through controlled intra-pore Si-H bond dissociation. This modulation enables precise control over the density of dangling states, facilitating the tunability of po-Si conductance. Theoretically evaluating the current-voltage characteristics of our proposed po-Si based synaptic devices, we determine the potential range of obtainable conductivity. Finally, we evaluate the performance by integrating porous silicon nanoelectronics devices into neural networks. These devices exhibit superior synaptic plasticity, faster response times, and reduced power consumption compared to other synapses. The research indicates that porous-silicon devices are highly effective in neuromorphic systems, paving the way for more efficient and scalable neural networks. These advancements have significant practical and cost-effective implications for a wide range of applications, including pattern recognition, machine learning, and artificial intelligence. Overall, our analyses reveal that the integration of po-Si based synaptic devices into the neural fabric offers a path towards achieving significantly denser and more energy-efficient neuromorphic hardware. With its tunable properties, large surface area, and potential for controlled conductance, po-Si emerges as a promising candidate for the development of advanced silicon-based nano-electronic devices tailored for neuromorphic computing. As we delve deeper into the potentials of po-Si, the era of cognitive computing, inspired by the elegance of bio-mimetic neural networks, edges closer to becoming a reality.
Capacitorless Power Electronics Converters Using Integrated Planar Electro-Magnetics
(2024-08) Kanakri, Haitham; Cipriano Dos Santos, Euzeli, Jr.; Rizkalla, Maher; Li, Lingxi; King, Brian
The short lifespan of capacitors in power electronics converters is a significant challenge. These capacitors, often electrolytic, are vital for voltage smoothing and frequency filtering. However, their susceptibility to heat, ripple current, and aging can lead to premature faults. This can cause issues like output voltage instability and short circuits, ultimately resulting in catastrophic failure and system shutdown. Capacitors are responsible for 30% of power electronics failures. To tackle this challenge, scientists, researchers, and engineers are exploring various approaches detailed in technical literature. These include exploring alternative capacitor technologies, implementing active and passive cooling solutions, and developing advanced monitoring techniques to predict and prevent failures. However, these solutions often come with drawbacks such as increased complexity, reduced efficiency, or higher upfront costs. Additionally, research in material science is ongoing to develop corrosion-resistant capacitors, but such devices are not readily available. This dissertation presents a capacitorless solution for dc-dc and dc-ac converters. The proposed solution involves harnessing parasitic elements and integrating them as intrinsic components in power converter technology. This approach holds the promise of enhancing power electronics reliability ratings, thereby facilitating breakthroughs in electric vehicles, compact power processing units, and renewable energy systems. The central scientific premise of this proposal is that the capacitance requirement in a power converter can be met by deliberately augmenting parasitic components. Our research hypothesis that incorporating high dielectric material-based thin-films, fabricated using nanotechnology, into planar magnetics will enable the development of a family of capacitorless electronic converters that do not rely on discrete capacitors. This innovative approach represents a departure from the traditional power converter schemes employed in industry. The first family of converters introduces a novel capacitorless solid-state power filter (SSPF) for single-phase dc-ac converters. The proposed configuration, comprising a planar transformer and an H-bridge converter operating at high frequency, generates sinusoidal ac voltage without relying on capacitors. Another innovative dc-ac inverter design is the twelve step six-level inverter, which does not incorporate capacitors in its structure. The second family of capacitorless topologies consists of non-isolated dc-dc converters, namely the buck converter and the buck-boost converter. These converters utilize alternative materials with high dielectric constants, such as calcium copper titanate (CCTO), to intentionally enhance specific parasitic components, notably inter capacitance. This innovative approach reduces reliance on external discrete capacitors and facilitates the development of highly reliable converters. The study also includes detailed discussions on the necessary design specifications for these parasitic capacitors. Furthermore, comprehensive finite element analysis solutions and detailed circuit models are provided. A design example is presented to demonstrate the practical application of the proposed concept in electric vehicle (EV) low voltage side dc-dc power converters used to supply EVs low voltage loads.
Analysis of Latent Space Representations for Object Detection
(2024-08) Dale, Ashley Susan; Christopher, Lauren; King, Brian; Salama, Paul; Rizkalla, Maher
Deep Neural Networks (DNNs) successfully perform object detection tasks, and the Con- volutional Neural Network (CNN) backbone is a commonly used feature extractor before secondary tasks such as detection, classification, or segmentation. In a DNN model, the relationship between the features learned by the model from the training data and the features leveraged by the model during test and deployment has motivated the area of feature interpretability studies. The work presented here applies equally to white-box and black-box models and to any DNN architecture. The metrics developed do not require any information beyond the feature vector generated by the feature extraction backbone. These methods are therefore the first methods capable of estimating black-box model robustness in terms of latent space complexity and the first methods capable of examining feature representations in the latent space of black box models. This work contributes the following four novel methodologies and results. First, a method for quantifying the invariance and/or equivariance of a model using the training data shows that the representation of a feature in the model impacts model performance. Second, a method for quantifying an observed domain gap in a dataset using the latent feature vectors of an object detection model is paired with pixel-level augmentation techniques to close the gap between real and synthetic data. This results in an improvement in the model’s F1 score on a test set of outliers from 0.5 to 0.9. Third, a method for visualizing and quantifying similarities of the latent manifolds of two black-box models is used to correlate similar feature representation with increase success in the transferability of gradient-based attacks. Finally, a method for examining the global complexity of decision boundaries in black-box models is presented, where more complex decision boundaries are shown to correlate with increased model robustness to gradient-based and random attacks.
Efficient Intelligence Towards Real-Time Precision Medicine With Systematic Pruning and Quantization
(2024-08) Karunakaran, Maneesh; Zhang, Qingxue; King, Brian; Maher, Rizkalla E.
The widespread adoption of Convolutional Neural Networks (CNNs) in real-world applications, particularly on resource-constrained devices, is hindered by their computational complexity and memory requirements. This research investigates the application of pruning and quantization techniques to optimize CNNs for arrhythmia classification using the MIT-BIH Arrhythmia Database. By combining magnitude-based pruning, regularization-based pruning, filter map-based pruning, and quantization at different bit-widths (4-bit, 8-bit, 2-bit, and 1-bit), the study aims to develop a more compact and efficient CNN model while maintaining high accuracy. The experimental results demonstrate that these techniques effectively reduce model size, improve inference speed, and maintain accuracy, adapting them for use on devices with limited resources. The findings highlight the potential of these optimization techniques for real-time applications in mobile health monitoring and edge computing, paving the way for broader adoption of deep learning in resource-limited environments.
Enhanced 3D Object Detection and Tracking in Autonomous Vehicles: An Efficient Multi-Modal Deep Fusion Approach
(2024-08) Kalgaonkar, Priyank B.; El-Sharkawy, Mohamed; King, Brian S.; Rizkalla, Maher E.; Abdallah, Mustafa A.
This dissertation delves into a significant challenge for Autonomous Vehicles (AVs): achieving efficient and robust perception under adverse weather and lighting conditions. Systems that rely solely on cameras face difficulties with visibility over long distances, while radar-only systems struggle to recognize features like stop signs, which are crucial for safe navigation in such scenarios. To overcome this limitation, this research introduces a novel deep camera-radar fusion approach using neural networks. This method ensures reliable AV perception regardless of weather or lighting conditions. Cameras, similar to human vision, are adept at capturing rich semantic information, whereas radars can penetrate obstacles like fog and darkness, similar to X-ray vision. The thesis presents NeXtFusion, an innovative and efficient camera-radar fusion network designed specifically for robust AV perception. Building on the efficient single-sensor NeXtDet neural network, NeXtFusion significantly enhances object detection accuracy and tracking. A notable feature of NeXtFusion is its attention module, which refines critical feature representation for object detection, minimizing information loss when processing data from both cameras and radars. Extensive experiments conducted on large-scale datasets such as Argoverse, Microsoft COCO, and nuScenes thoroughly evaluate the capabilities of NeXtDet and NeXtFusion. The results show that NeXtFusion excels in detecting small and distant objects compared to existing methods. Notably, NeXtFusion achieves a state-of-the-art mAP score of 0.473 on the nuScenes validation set, outperforming competitors like OFT by 35.1% and MonoDIS by 9.5%. NeXtFusion's excellence extends beyond mAP scores. It also performs well in other crucial metrics, including mATE (0.449) and mAOE (0.534), highlighting its overall effectiveness in 3D object detection. Visualizations of real-world scenarios from the nuScenes dataset processed by NeXtFusion provide compelling evidence of its capability to handle diverse and challenging environments.

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