Theses (Electrical and Computer Engineering)

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Privacy-preserving protocols: Advancing security and flexibility with policy-based sanitizable signatures and fair exchange mechanisms
(2025) Afia, Ismail Sami Abdelaziz; AlTawy, Riham
This dissertation presents advancements in privacy-preserving protocols, focusing on two research areas: policy-based sanitizable signature schemes and fair exchange mechanisms. Sanitizable signature schemes allow designated parties to modify or sanitize signed messages while preserving the message’s authenticity. We present the Unlinkable Policy-Based Sanitizable Signature (UP3S) scheme, which addresses a significant deficiency in existing policy-based sanitizable signature schemes, the lack of unlinkability. A crucial security property, particularly in privacy-sensitive applications, unlinkability guarantees that distinct sanitized versions of a given message cannot be linked to the original message or to each other, even across multiple sanitization operations. Building upon UP3S, we investigate extending its capabilities to support fine-grained control over message modifications. This involves enabling multiple modification policies for a single message and facilitating the delegation of sanitization rights. To this end, we propose the Traceable Policy-Based Signature (TPBS) scheme, which forms the basis for the Extended Policy-Based Sanitizable Signature (EP3S). EP3S offers a flexible and secure framework for policy-based sanitizable signatures, incorporating enhanced control over message modifications and sanitization-rights delegation. In the area of fair exchange mechanisms, our contributions focus on privacy-preserving exchanges of both digital and physical assets. We introduce V2VFx, a privacy-preserving framework for the fair exchange of physical assets, specifically in vehicle-to-vehicle energy trading. Together, these contributions advance the state of privacy-preserving protocols by addressing key limitations in existing schemes and extending their applicability.
Intelligent microscopic models for traffic flow characterization
(2025) Ali, Faryal; Gulliver, T. Aaron; Khan, Zawar
In this dissertation, microscopic models for traffic flow characterization are studied. Based on the traffic flow evolution characteristics and aiming to characterize the traffic behavior accurately and realistically, this research focuses on developing realistic traffic flow models to improve traffic safety, efficiency, and pollution control. A traffic model based on driver response is introduced considering both driver reaction and sensitivity. Driver sensitivity includes typical, sluggish, or aggressive drivers. Then the pavement condition is investigated using the pavement condition index (PCI). The impact of fog on visibility is a major factor affecting traffic congestion and safety. Thus, the traffic behavior based on visibility during foggy weather is also investigated. In addition, the recent introduction of connected autonomous vehicles (CAVs) has had a significant impact on road networks. Therefore, a spring-mass based traffic model to evaluate human-driven vehicle (HV), autonomous vehicle (AV), and CAV behavior on a horizontal curve is proposed. Further, CAV behavior at bottlenecks considering cyberattacks is investigated. This dissertation also provides an energy consumption model considering driver energy-saving awareness. The performance of traffic models is presented and compared with the intelligent driver (ID) model, and traffic stability is analyzed. The results demonstrate the advantages of the proposed approach.
Study of protein-small molecule interaction via nanoaperture optical tweezer
(2025) Yang-Schulz, Annie; Gordon, Reuven
This thesis explores small molecule-protein interactions using nanoplasmonic optical tweezers. Since their inception by Ashkin, optical tweezers have been widely adopted in biology due to their unique ability to manipulate nanoscale objects. The transcriptional activity of a single RNA polymerase molecule has been measured using dual optical tweezers and DNA tethering, revealing key processes such as transcriptional stepping, pausing, backtracking, and termination. However, tether-free nanoscale trapping remains challenging with single-beam optical tweezers, as trapping forces decrease disproportionately with target size reduction. A major breakthrough occurred with the development of subwavelength apertures for field enhancement, enabling trapping of free solution single proteins. Our lab specifically employs double nanohole apertures to generate a highly confined gradient field, facilitating stable trapping at the nanoscale. Abnormal protein phosphorylation plays a critical role in chronic illnesses such as Alzheimer’s disease, cancer, and arthritis. Consequently, both kinase and phosphatase therapeutics have become major areas of research. While numerous kinase inhibitors have received FDA approval, phosphatase-targeting drugs have faced significant challenges due to difficulties in identifying effective and selective binding sites. This thesis explores a promising phosphatase-targeting cancer therapeutic candidate using subwavelength-assisted optical tweezers. We present qualitative insights into the structural impact induced by small-molecule binding, complemented by molecular dynamics simulations. Additionally, we quantify binding affinity at both the single-molecule and ensemble levels
Novel methods for optical trapping and raman spectroscopy of nanoparticles
(2025) Khosravi, Behnam; Gordon, Reuven
This dissertation explores advanced novel techniques in optical trapping and Raman spectroscopy, focusing on the utilization of double-nanohole (DNH) apertures. We investigate polarization selective reflection mode optical trapping, which enhances the precision and efficiency of nanoparticle manipulation compared to conventional transmission mode optical trapping. These studies investigated Raman spectroscopy with DNH trapping, demonstrating significant Raman signal enhancement due to the intense electric fields generated within the DNH gaps. This enhancement is quantified through Raman signal enhancement by using DNHs and providing insights into the mechanisms driving this phenomenon. Additionally, we present methodologies for observing trapping in real-time using the camera in optical tweezer systems, enabling direct visualization and analysis of the trapping event. Complementary to the experimental work, DNH simulations are conducted to model the optical and plasmonic properties of the DNH structures. These simulations offered a theoretical framework that supports and explains the experimental findings. The integration of these techniques not only advances the field of optical trapping and Raman spectroscopy but also creates new opportunities for applications in nanotechnology and materials science.
Automatic characterization of surface-breaking crack defects in pipe walls using ultrasound images
(2025) Govor, Vladislav; Rakhmatov, Daler N.
Ultrasound imaging is a widely used technique in non-destructive testing for detecting and sizing defects in industrial pipelines. Accurate defect localization and sizing are critical for diagnosing the structural integrity of pipelines and preventing failures that pose significant environmental, economic, and safety risks. Motivated by the need to mitigate such risks, this thesis presents an effective technique for localization and sizing of surface-breaking cracks on the outer walls of liquid-filled pipes. The proposed approach combines traditional and novel data processing techniques applied to a sequence of multi-view ultrasound image frames of inspected pipe sections. Tri-sectional sliding windows are utilized for frame-by-frame view-specific defect localization, followed by establishing correspondence among potentially differing crack location estimates across all considered views and frames, as well as for defect sizing. Additionally, a similarity-based sizing method is developed to increase the accuracy by comparing synthetic images to the original ones. Our evaluation results using real-world experimental data demonstrate that the sliding window method is computationally inexpensive and yields accurate localization and sizing results in most cases, while the similarity-based method provides superior sizing accuracy in more complex scenarios.
1-D and 2-D digital filters design using model reduction and optimization methods for broadband beamforming and interference rejection
(2025) Omar, Abdussalam; Agathoklis, Panajotis; Shpak, Dale John
This thesis presents several design algorithms for nearly linear-phase one-dimensional (1-D) and two-dimensional (2-D) infinite impulse response (IIR) digital filters. Optimization techniques as well as model order reduction (MOR) filter design methods are considered in this study. For 1-D, finite impulse response (FIR) filters can achieve perfectly linear phase which makes them important in applications such as the field of audio signal processing where a flat delay characteristic may be desired. However, in most applications a perfectly linear phase response is not required and filters that have nearly linear phase response are quite acceptable. In such cases, IIR filters are more attractive than FIR filters. The design of IIR filters is more challenging than that of FIR filters because it results in a highly nonlinear objective function that requires sophisticated optimization methods. The 1-D optimization method proposed here solves the problem of approximating specified magnitude and linear-phase responses simultaneously. Since IIR filters can be designed to have nearly linear phase response in the passband, their passband group delay is usually considerably smaller than the delay of linear-phase FIR filters with equivalent magnitude responses. Meeting a required minimum stopband attenuation or a minimum deviation from the desired magnitude and phase responses in the passbands are common design constraints that can be handled by the proposed optimization method for 1-D IIR filter. Also, an important constraint in the design of IIR filters is the prescription of a maximum pole radius, which allows to guarantee the stability margin and low coefficient selectivity for the obtained filter for finite-precision implementations. These design specifications are consistent with the constraints which often arise in practical filter design problems. In this research work, an optimization method for solving this constrained 1-D IIR design problem is presented. The above optimization method used for designing 1-D IIR filters is extended to 2-D separable-denominator IIR digital filters with nearly linear phase in the passband. During the development of the proposed design techniques for 2-D digital filters, a special emphasis has been placed on their computational efficiency and a method for the design of 2-D IIR digital filters based on a balanced realization (BR) model order reduction technique is proposed. In this method, the initial design is a linear phase 2-D FIR filter realized in a 2-D state space model, which leads to a stable 2-D separable-denominator IIR filter with nearly linear phase in the passband. The model reduction method is based on structured controllability Ps and structured observability Qs gramians. These gramians are block-diagonal positive-definite matrices satisfying 2-D Lyapunov inequalities. An efficient general algorithm is developed to compute these matrices by minimizing the trace of Ps and the trace of Qs under Linear Matrix Inequalities (LMI) constraints. The use of these gramians ensures that the resulting 2-D IIR filter is a 2-D stable filter. Furthermore, the obtained nearly linear-phase 2-D IIR filter is more economical and computationally more efficient than the original 2-D FIR filter. Numerical examples using MATLAB show that the proposed method provides a good compromise between the filter selectivity and computational complexity when compared to existing techniques, making the results of this dissertation directly applicable to many practical applications. For example, in the field of array signal processing, 2-D digital filters having a fan-shaped filter in the passband emerge as powerful tools, particularly when employed as beamformers in scenarios where the Direction of Arrivals (DOAs) of the desired broadband Plane Waves (PWs) are known. In such cases, the designed 2-D FIR and IIR filters having a fan-shaped filter passband in the 2-D frequency domains are used as beamformers. Benefiting from the knowledge of DOAs of the desired broadband PWs, these filters are used to extract the signal of interest (SOI), suppress the interference, and reduce the noise corrupting the SOI. The successful implementation of 2-D FIR and IIR fan filters as beamformers not only enhances the rejection of the interference but also demonstrates its capability to reduce the effect of AWGN. This dual functionality holds significant implications for practical applications in digital signal processing, in which robustness against interference and noise is important. Simulation results demonstrate a good performance of the proposed beamformers and confirmed that the filters obtained using the proposed methods are capable of extracting and enhancing the desired 2-D broadband signals according to their directions of arrival under severe interference and noise.
Detection of fraudulently recycled integrated circuits
(2025) Dimopoulos, Alexandros; Sima, Mihai; Neville, Stephen William
As is true of many types of products, integrated circuits (ICs) are subject to counterfeiting. Of the various methods of counterfeiting ICs, recycling, where still functioning units are recovered from waste streams and passed off as new, is particularly problematic. These components are still functional but are unreliable due to their unknown operational histories. This poses an important risk to critical infrastructure in domains ranging from defense, to health care, to transportation. The long lifetimes of these systems makes it challenging to find suitable replacement ICs since the electronics industry evolves rapidly and produces parts with comparatively short production lives. A number of recycled integrated circuit (IC) risk mitigation approaches have been proposed, but these generally lack pragmatic feasibility. This work proposes a novel real-world deployable on-chip sensor that: 1) is tamper-resistant by exploiting persistent changes caused by hot carrier injection (HCI); 2) generates a DC signal measurable by common low-cost test equipment; and 3) reuses an existing I/O interface, including existing pins; while 4) requiring a very small footprint. Combining this sensor with a random sample-based testing strategy allows for low-cost and time efficient detection of fraudulently recycled batches of ICs. Employing a random sample size as small as 130 is sufficient for identifying such batches with a statistical significance level of 0.01. This is demonstrated through simulation-based validation using process-accurate models of a 65 nm technology. The design of countermeasures against IC recycling requires the ability to simulate aging in CMOS devices. Electronic design automation tools commonly provide this ability; however, their models must be tuned for use with a specific target technology. This requires data which is ideally provided by a fab. It may also be collected from a set of purpose-built test devices, a costly and time-consuming process. This work describes a novel, low-cost, and rapid approach to tuning such models. It is an iterative method that leverages public domain data sourced from published studies to fit an aging model. Results are statistically validated against the target technology's specification. The approach is demonstrated by fitting a compact hot carrier injection degradation model for use with both core and I/O nMOSFETs from a specific 65 nm technology. Resulting model parameter values are validated with a maximum error of 0.5% with a 99% confidence bound.
Deep learning methods for mitigating catastrophic forgetting in medical imaging
(2025) Javadinia, Samaneh; Baniasadi, Amirali
Continual learning allows machine learning models to learn new tasks incrementally without losing previously acquired knowledge, a capability crucial in medical imaging where data evolves over time. A persistent challenge in this field is catastrophic forgetting, where models overwrite past knowledge when learning new tasks, limiting their practical use in dynamic environments. This thesis introduces a new framework called CLFCR-MC (Continual Learning Framework with Contrastive Regularization), specifically designed to tackle catastrophic forgetting in medical imaging applications. By combining momentum contrastive learning and a custom loss function that integrates classification, cosine similarity, and distillation losses, CLFCR-MC enhances the model’s ability to retain previous knowledge while adapting to new tasks. Experiments using medical imaging datasets, such as BloodMNIST and PathMNIST, demonstrate that this framework significantly reduces forgetting and improves accuracy compared to existing methods. These findings highlight the potential of CLFCR-MC to address real-world challenges in continual learning and improve diagnostic capabilities in healthcare.
Facilitating detection and sizing of crack defects in pipes by 3D K-means clustering
(2024) Mazinani, Fatemeh; Rakhmatov, Daler N.
This thesis presents a novel approach for detection and sizing of surface-breaking crack defects in pipes using 3D K-Means clustering of ultrasound imaging data. The proposed method processes volumetric ultrasound data (obtained from a moving transducer array inside a pipe) to identify distinct clusters, effectively reducing noise and isolating critical crack-related features. Experimental validation has been performed on three pipe samples with different crack sizes and locations. The results show that 3D K-Means clustering improves crack detection and sizing, outperforming 2D K-means clustering in most cases. This research contributes to the field of ultrasonic nondestructive testing by providing an efficient solution for assessing the structural integrity of critical infrastructure components, such as pipelines.
Cavity optomechanical oscillation locking
(2024) Gan, Jinshuai; Lu, Tao
Optical microcavities have emerged as powerful tools for detecting single molecules and nanoparticles due to their exceptional sensitivity and label-free operation. However, the performance of ultra-high-Q microcavities is highly sensitive to factors such as ambient temperature fluctuations, mechanical vibrations, and laser frequency drifts, all of which destabilizing laser-cavity detuning and intracavity power. Optomechanical oscillation (OMO), a phenomenon driven by radiation pressure within the cavity, offers significant advantages for liquid-based sensing, but requires stringent conditions stable laser-cavity detuning for sustainable regenerative operation. In this thesis, we demonstrate stable, long-term OMO in an aqueous environment by implementing a Proportional-Integral (PI) locking
Multi-channel source separation with video data
(2024) Mosayyebpour, Sahand; Gulliver, Aaron
This research introduces a supervised multi-channel audio source separation system that integrates a video-based face detection system. The face detector identifies the nose position, aiding the multi-channel processing in isolating the primary speaker while suppressing environmental background noise and distracting secondary speakers. It is demonstrated that in far-field applications, multi-channel processing struggles with distracting secondary speakers when the primary speaker position is unknown. Utilizing video data provides valuable insights to identify the target speaker and assists the audio source separation system in directing its focus towards the target speaker. Furthermore, it is shown that multi-channel processing benefits from speaker position information to improve noise reduction in noisy reverberant environments.
Vision transformer-based context-aware system for lingual ultrasound in digital health ecosystem
(2024) Al-hammuri, Khalid; Gebali, Fayez; Kanan, Awos
The complex nature of modern healthcare systems and the widespread distribution of healthcare infrastructure made the interoperability within healthcare information system challenging. This could poses security risks, missing data, miscommunication , in addition to the human and technical-based errors. This dissertation focuses on utilizing an advanced AI system to overcome the challenges of clinical analysis, data confidentiality, availability, and integrity. There are three main contributions of this research. First, implement TongueTransUNet, which is a well-managed architecture that utilizes a vision transformer, UNet encoder-decoder convolutional neural network, contrastive loss and quality control process supported with human-reinforcement feedback to extract tongue fingerprint. Second, design ZTCloudGuard for access control within the telehealth cloud-based eco-system between. The architecture manage users, devices, and output attributes by deriving a score to assess the mutual relationship considering semantic and syntactic analysis. Third, utilize hybrid qualitative and quantitative evaluation metrics and conduct comparative analysis to other related research. The main applications to this research are minimizing medical errors, protecting healthcare practitioners, detecting unrelated input and undesired output. An ablation study using synthetic healthcare information attributes and word2vec model was conducted to judge the model results. The outcomes showed robustness and enhancement by focusing on high-quality input and rejecting unacceptable data. If the automatic process fails or goes below a predefined threshold, an extra reinforcement verification layer is introduced to the algorithm to add manual and human feedback.
Label-free study of Bovine Serum Albumin conformational dynamics using optical nanotweezers and experimental analysis of asymmetric DNH nanostructures
(2024) George, Sherin; Gordon, Reuven
This thesis concerns the label-free investigation of Bovine Serum Albumin (BSA) conformational dynamics and the interaction of different proteins with gold surfaces using Double Nanohole(DNH) optical tweezers. It is divided into two parts: Study of conformational changes of labeled and unlabelled BSA and investigation of electrostatic interactions between proteins and gold surfaces. The Gibbs energy for the N to F states was calculated for both labeled and unlabelled BSA from the free energy diagram and the values show a small effect of labeling on the protein. This Chapter also explored the effect of electrostatic interactions between the gold surface and proteins with different charges on the trapping stiffness. Further, the experimental investigation of the development of a new nanostructure called Asymmetric Double Nanohole and its possible application in optical trapping are described in this thesis. Fabrication techniques and result analysis are described, even though the trapping efficiency turned out to be the same as that for traditional DNH structures.
Library usage analysis in the C++ codebase of Fedora Linux 37
(2024) Deng, Jiachao; Adams, Michael D.
C++ source code analysis is conducted at scale. A framework is proposed for analyzing the C++ codebase of operating systems that employ the dnf package manager, such as Fedora Linux and Red Hat Enterprise Linux. The framework can run an arbitrary static analysis tool over software packages that contain C++ code from compatible operating systems. In order to evaluate the effectiveness of the framework and to better understand how the C++ language is used in practice, a C++ analysis tool is developed to study library usage with a fine level of granularity, considering instances of uses of types, type aliases, member/non-member functions, variables, and enumerators. Our framework, combined with the C++ library usage analysis tool, is used to analyze 2 379 software packages from the codebase of Fedora Linux 37. The number of packages analyzed is two to three orders of magnitude larger than that of previous C++ research. We applied our library usage analysis tool to nearly 400 million lines of C++ code across these packages. Leveraging the Clang compiler front-end libraries, our tool extracts information from correctly parsed C++ code, which is an improved approach compared to many existing studies. As a result, the tool provides an accurate collection of library usage instances from C++ software. Numerous observations are made regarding various aspects of library usage that can facilitate improved teaching of C++, aid in the refinement of C++ libraries, and help guide the future evolution of the C++ standard. For example, our analysis reveals that C++ programmers rarely use some C++ standard library algorithms designed for specialized purposes or combined operations. These algorithms often appear in less than 1% of all C++ software packages investigated. We suggest that the standard library exercise caution when adopting infrequently needed algorithms to maintain a streamlined interface. Such observations summarize current trends in C++ library usage and provide recommendations for improving the C++ language and its libraries.
Decoding illicit bitcoin transactions: A multi-methodological approach for anti-money laundering and fraud detection in cryptocurrencies
(2024) Shojaeinasab, Ardeshir; Najjaran, Homayoun
This dissertation examines the challenges of detecting illicit activities in cryptocurrency transactions, with a focus on Bitcoin. It begins by analyzing cryptocurrency mixing services and their obfuscation techniques. The research then provides a comprehensive evaluation framework for these services, conducting an assessment of all available services and academic proposals. Following this, the study introduces a novel framework that uses statistical patterns to identify potential money laundering and clustering cryptocurrency addresses that can reveal real-world identities involved in illicit transactions. The study then leverages the Elliptic dataset, a graph representation of Bitcoin transactions, to classify illicit activities. While classical machine learning methods struggled with the imbalanced nature of financial fraud data, Graph Neural Networks (GNNs) - specifically Graph Convolutional Networks and Graph Attention Networks - proved more effective. By considering the graph topology and connections between nodes, GNNs significantly reduced false negative rates in detecting illicit transactions. To enhance transparency, the research employs Explainable AI techniques, particularly SHAP values, to interpret the decision-making process of GNN models. This approach not only improves model trustworthiness but also provides insights into the key features and graph structures that contribute to illicit activity detection. The thesis concludes by presenting a comprehensive toolkit for combating digital financial crimes. It demonstrates that despite the perceived anonymity of blockchain technology, effective methods exist to unveil illicit activities, thus enhancing the security and integrity of cryptocurrency transactions. This work bridges the gap between technological advancement and regulatory compliance, establishing a new standard in the fight against cryptocurrency-based crime.
QoS-oriented multipath protocol design in mobile networks
(2024) Yang, Wenjun; Cai, Lin
Emerging applications demand stringent quality of services (QoS). Meanwhile, future networks are featured by ubiquitous mobility. How to meet users' QoS requirements in highly mobile environments remains an open issue, which motivates our research on QoS-oriented multipath transport layer protocol design in mobile networks. First, multipath transfer is promising in tackling mobility issues for a seamless handoff. Scheduling packets across multiple paths, however, has the issue of out-of-order (OFO) arrival due to the heterogeneity of the paths. In this regard, we put forward a Mobility-Aware Multipath Scheduler (MAMS), ensuring that the reordering delay of each packet is minimized in various mobility scenarios and thus the QoS is significantly improved. Enabling multipath transfer in the Integrated Terrestrial and LEO Satellite Network (ITSN) is promising. However, the existing multipath congestion control algorithms in ITSN suffer from bandwidth under-utilization or overshooting issues due to the high-speed network movement. Therefore, a novel Mobility-Aware COngestion control (MACO) algorithm is developed. As applications are the driving force for protocol design, we investigate the performance of video streaming applications using multipath transfer. Assuming the QoS requirements of the application are known by the sender, we adopt a lightweight learning framework, a contextual multi-armed bandit (CMAB), to discover the underlying relationship between dynamic network states and QoS performance, which can intelligently select access networks and adapt FEC coding to trade off delay, reliability, and throughput. Furthermore, 360-degree videos are not only bandwidth-intensive but also highly sensitive to delays. Ensuring both high video quality and smooth playback experience remains a critical issue. Therefore, we introduce a QoE-oriented Deadline-driven (RIDE) algorithm for multipath scheduling at the frame level. RIDE employs a dependency tree to understand deadlines for different types of frames and considers the negative impacts of Field of View (FoV) changes on scheduling decisions. Utilizing an actor-critic framework to train the neural network enables the scheduler agent to adapt to dynamic environments, including network and FoV dynamics.
Lightweight deep learning model for nondestructive evaluation of crack defects
(2024) Jia, Yixiang; Rakhmatov, Daler N.
Ultrasonic nondestructive evaluation (NDE) is an essential tool in various industries including aerospace, energy, and civil engineering, for assessing the structural integrity of manufactured products without damaging them. This thesis is focused on the automated analysis of ultrasonic NDE data by means of low-cost machine learning (ML) techniques, particularly in the context of inline pipeline inspection. We propose two lightweight neural network architectures for efficient multi-attribute classification to characterize surface-breaking crack defects in terms of their location, size, and tilt. Our networks have under 2M parameters and incorporate novel design elements inspired by the latest MobileNet models. Their computational footprint is also small, not exceeding 100M floating-point operations (FLOPs) per data sample. The proposed models process raw channel data acquired by a transducer array, as opposed to multi-view beamformed image patches utilized in related works, thus eliminating the computational burden associated with image reconstruction. Our evaluation results, based on a public-domain NDE dataset, demonstrate that our networks offer a balanced combination of their competitively high classification performance and low cost. These findings highlight the potential of lightweight deep learning models in ultrasonic NDE data analysis, which contributes to the development of more advanced and intelligent inspection systems.
Ergodicity in software systems
(2024) Nikdel, Zahra; Neville, Stephen William
This dissertation applies dynamical systems theory (DST) to formally investigate the statistical run-time performance predictability of arbitrary scale software-centric systems, ranging from small-scale embedded systems to modern large-scale cloud-deployed container and virtual machine based distributed systems. The research focuses on verifying Birkhoff’s Ergodic Theorem (BET) compliance for queuing network (QN) models of deployed software systems against BET-compliant Poisson and bursty incoming workloads. The approach applies a previously developed extension of Maurer’s Turing-reducible computer model, termed the Extended Maurer Model (EMM), as the requisite bridge between classical QN software system models and the DST-based BET tenets underlying classical Markovian QN analysis approaches. Moreover, it is shown that as the EMM describes a $\sigma$-finite measure space, a known ergodicity equivalency theorem can be used to develop a formal DST analysis approach to prove when BET left-hand and right-hand side conditions can be met for run-time software systems performance measures. More specifically, formally proving recurrence holds within large-scale systems, as required by classical Markovian analyses, has remained an open problem. By comparison, this research shows that this issue can be addressed by instead assessing when and why: i) wandering sets of non-zero measure arise and ii) event space variations and non-invariant DST measures arise, given (i) and (ii) are mathematically known to be equivalent to assessing recurrence within $\sigma$-finite measure spaces. This theoretical DST analysis of QNs defined over the EMM representation of run-time software system behaviors then leads to the development of four pragmatically easily measurable and implementable software engineering design rules that can be used to assess when and why a given deployed software system will (or will not) exhibit statistically predictable run-time behaviors. These design rules are then applied to develop a sufficiently rich cloud-deployed software system simulation framework, which includes incoming statistical workloads, cloud networking fabric, physical server, virtual machine, and container deployment regimes, fair and real-time OS scheduling, and background physical server workloads. This simulation framework is then used to validate the DST theory BET-compliance analysis insights through a detailed set of software system run-time deployment scenarios, both for an industry-held exemplar system and for emerging industry deployment trends. To our knowledge, this is the first set of research to formally assess run-time software system BET-compliance for systems of arbitrary scale and complexities. Moreover, it is the first work to show, through theory and simulation-based validation, that modern software systems exist as highly complex dynamical systems that can concurrently admit BET-compliant and BET non-compliant performance measures, while also admitting measures that can dynamically transition back and forth between BET-compliance and non-compliance as the system runs. As engineering can be summarily defined in terms of the need to build systems and solutions that behave predictably in their real-world operation under all likely deployment scenarios and environments, the developed novel insight into the core DST complexity of software systems helps to partially explain why the software engineering of modern industry-scale software systems has remained a challenging and largely open problem, outside of specific quite tailored regimes, i.e., the regimes that can be seen to follow this dissertation's developed software engineering design rules. The issue of BET-compliance has wide applicability across many areas, spanning statistical run-time performance predictability, control theory, machine learning (ML) and artificial intelligence (AI), quantum computing, etc. The insights and theoretical framework developed in this research have wide potential applicability beyond just the core software engineering need to develop and deploy software systems that behave predictably, in the sense of remaining within a defined set of statistical behavioral bounds. More particularly, emerging areas of potentially applicable research include: the use of formal control theory approaches within cloud elastic services; the safety and operationally critical aspects of Smart Cities, Smart Grids, autonomous vehicles and vehicle networks; when, why, and how AI/ML approaches can be applied to accurately predict software system run-time behaviors; BET-compliance within cyber-security focused regimes and solutions; etc. As such, we hope this work will be seen as a useful and insightful contribution to advancing the formal engineering of software-centric systems and solutions.
Design and testing of a terahertz bandstop filter using varying radii split-ring resonators
(2024) Asadi, Saeid; Darcie, Thomas Edward; Smith, Levi
The terahertz (THz) band which ranges from 0.1 THz to 10 THz has been relatively unexplored when compared to other frequency bands due to the unavailability of sources and detectors. However, continual advances in technology have made this frequency band more accessible and have attracted attention because of unique applications such as imaging, spectroscopy, communications, and physical defect detection. In many applications throughout the electromagnetic spectrum signal filtering is used to improve signal-to-noise ratios. One method to filter signals is using split-ring resonators (SRRs) that are made of nonmagnetic metals which respond to electromagnetic waves like a magnetic medium. SRRs at their resonant frequency create a magnetic dipole so that they can stop or pass electromagnetic waves. This property has made SRRs a commonplace element in the design of metamaterials (MTMs). This thesis does not focus on the investigation of MTM properties and characteristics (negative permittivity or negative permeability), but it does provide some of the relevant background and theory. This thesis reports a proof-of-concept terahertz band-stop filter constructed from SRRs that has a center frequency of 1.06 THz and a -3 dB bandwidth of 0.36 THz. The design consists of nine SRRs of varying radii (3×13 µm, 3×14 µm, 3×15 µm) that are placed between the conductors of a coplanar stripline (CPS). The response of the filter is measured using a modified terahertz time-domain spectrometer and a reasonable agreement between simulation and experiment was found. This work demonstrates the viability of using varying-radii SRRs as discrete sub-wavelength filter elements for THz systems. In addition, the ABCD matrix approach was utilized to get the transmission response of the equivalent circuit. This filter was fabricated using gold on a thin Si3N4 substrate, and the simulated data are in good agreement with the experimental results.
Feasibility assessment of offsetting diesel generator in an islanded microgrid
(2024) Rahbar, Reza; Baniasadi, Amirali; Chelvan, Ilamparithi Thirumarai
The presented work endeavors to mitigate the reliance on existing diesel generators by integrating hybrid renewable energy systems to fulfill the energy demands of a small community residing in Meziadin Lake, Canada. Utilizing HOMER Pro 3.16.2 software, the research employs a range of analyses, encompassing Net Present Value (NPV), Cost of Energy (COE), emissions, energy production, and operational costs. The simulation outcomes indicate that the combination of solar energy, batteries and the existing diesel generator represents the optimal solution, demonstrating superior performance with NPC and COE of $3.26M and $0.1815/kWh, respectively. Our model has a renewable fraction of 82% and results in 47.5% reduction of NPC and COE, as well as 89.1% decrease in annual carbon dioxide emission compared to the baseline system.

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