Electrical and Computer Engineering

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https://hdl.handle.net/10315/35603

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Open Access
ARC-C: Analytical Framework and Software Tool for Automated Risk-Based Cryptoperiod Calculation in Industrial Control Systems
(2025-04-10) Cianfarani, Gabriele Alberto; Vlajic, Natalija
Over the past decade, industrial control systems (ICSs) and critical infrastructure (CI) have become prime targets for advanced persistent threat (APT) groups and nation-state actors due to their potential for severe impact. This has resulted in the cybersecurity community increasing their focus on ICS/CI threat modelling and defence. This thesis examines the crucial role of the internal network reconnaissance stage of ICS/CI attacks, particularly those using the OPC UA standard with encrypted in-transit data. We first introduce a comprehensive attack tree outlining data siphoning strategies and highlight the importance of periodic encryption-key rotation to mitigate risk. Noting the lack of clear cryptoperiod guidelines in industry standards, we then present the Automatic Risk-based Cryptoperiod Calculation (ARC-C) framework. ARC-C aims to optimally determine cryptoperiod lengths based on security risks and operational constraints. We demonstrate its application in two realistic ICS environments: a Water Treatment Plant and an Energy Storage System.
Open Access
Modeling of Eye contact behavior
(2025-04-10) Hosseini, Zahra; Troje, Nikolaus
With the rise of online platforms and avatar-based communication, understanding eye contact a key non-verbal cue is crucial for trust in conversations. This study examines eye contact behavior across face-to-face interactions, a screen-sized window interface, and online meetings. We collected twelve hours of eye contact data from 48 individuals using eye trackers and motion capture in dyadic settings. Our analysis showed consistent eye contact patterns in face-to-face and screen-sized window interactions, while online meetings caused significant shifts due to the lack of direct eye contact. To model this behavior, we trained a diffusion model (DDPM) to generate synthetic eye movements that preserved key features of real data. We evaluated our model using metrics such as eye contact frequency. This study provides insights into how communication media influence gaze behavior and explores methods for generating realistic eye movements in conversational settings.
Open Access
Encapsulating laser-induced graphene to enhance its electrical and mechanical properties
(2025-04-10) Bayat, Fatemeh; Pisana, Simone
Laser-induced graphene (LIG) has emerged as a promising material in the field of printed electronics, offering excellent electrical conductivity and versatility. Its versatility stems from multiple factors: the potential for low-cost, facile, and rapid production without toxic chemicals (unlike traditional copper traces on printed circuit boards); the ability to be fabricated on various substrates; its customizable properties and its large surface area. However, the inherent fragility of its structure poses a significant challenge, as it can be easily damaged or removed even by touching or under mechanical forces, limiting its practical applications. While a few encapsulation techniques have been explored to enhance the mechanical robustness of LIG, they often result in a significant increase in electrical resistance, diminishing its conductivity to the point where it becomes impractical for use as conductive traces in printed electronics. In this study, we present a novel and facile approach to encapsulating LIG inscribed on a Kapton substrate while preserving the LIG's remarkable electrical properties. Through a simple and cost-effective encapsulation process involving controlled pressure application, we successfully limited the increase in resistance to just 5\% of the LIG’s original value. This optimal result was achieved with an applied pressure of 80 psi using a hydraulic press. The fabricated LIG exhibited a low initial sheet resistance of approximately 2.2 $\Omega/ \text{sq}$, making it a promising candidate for applications requiring this level of resistance. Our approach offers a straightforward solution to enhancing the mechanical robustness of LIG while maintaining its desirable electrical characteristics, potentially broadening its applicability in flexible electronics and related fields. Comprehensive characterization techniques, including Raman spectroscopy and scanning electron microscopy (SEM) were employed to investigate the structural and morphological properties of the encapsulated LIG. The results obtained from these analyses validate the efficacy of our encapsulation approach in preserving the desirable properties of LIG while enhancing its mechanical durability. The findings of this research open up new avenues for the practical implementation of LIG in various electronic devices and applications where mechanical robustness and high conductivity are essential requirements, such as flexible and wearable electronics and flexible interconnects for conformable electronic systems. Future research endeavors can focus on further optimizing the encapsulation process, exploring alternative substrate materials, and investigating the potential integration of encapsulated LIG into diverse electronic systems and components.
Open Access
Deciphering Ion Channel Dynamics: A Clustering Approach for Signal Analysis
(2025-04-10) Kazemi, Mohammadreza; Eckford, Andrew W.
This thesis investigates clustering techniques for analyzing multi-channel CFTR ion channel activity recorded via the patch-clamp method. Accurately determining the number of active channels and classifying their states is crucial for understanding ion channel dynamics. Both classical and machine learning approaches are explored. Classical methods, including DBSCAN and a hybrid DBSCAN-BGMM approach, show limitations in handling noise and overlapping states, particularly with increasing channel counts. A novel machine learning approach combining a Cluster Count Neural Network (NN) for channel number estimation and a Long Short-Term Memory (LSTM) network for state classification demonstrates superior performance. The NN effectively captures underlying patterns while the LSTM leverages temporal dependencies, achieving higher accuracy and robustness even with complex, noisy signals. This research offers promising tools for analyzing ion channel activity and has implications for cystic fibrosis research and drug development.
Open Access
Energy-Efficiency-Optimized Convolutional Spiking Neural Networks for Patient-Specific Seizure Detection
(2025-04-10) Muneeb, Abdul; Kassiri, Hossein
This thesis presents the design, development, and hardware implementation of neuromorphic spiking convolutional neural networks (SNNs) for patient-specific seizure detection. Leveraging the energy-efficient, event-driven nature of SNNs, the work achieves a low-power, low-latency, high-accuracy processing unit for multi-channel implantable brain-machine interfaces. Initial validation on publicly available datasets using spiking convolutional neural networks (SCNNs) demonstrates average sensitivities and specificities of 83.02% and 86.31%, respectively, with a false positive rate of 0.69/hour. The 1-bit Integer-Net SCNN achieves sensitivity comparable to 32-bit CNNs while improving memory efficiency by 27× and energy efficiency by 98.6%, consuming just 1.28 μJ/classification. Further optimization tailored to patient-specific seizure detection on STM32H747 microcontrollers and Intel Loihi simulators achieves sensitivities of 92.2% and specificities of 97.3%. By optimizing quantization resolution, encoding schemes, and implementing SCNNs in HDL, this thesis advances energy-efficient seizure detection, contributing to adaptive brain interfaces and the broader field of neuromorphic computing.
Open Access
Patient-Optimized Temporally-Adaptive Neurostimulation for Epilepsy Using Deep-EDMD Koopman MPC
(2024-11-07) Salahi, Rojin; Kassiri, Hossein
This thesis presents the design and implementation of a closed-loop neurostimulator controller for delivering patient-optimized, temporally-adaptive stimulation pulses to control epileptic seizures. The method employs a predictive model and a Model Predictive Controller (MPC) to optimize stimulation. The predictive model, based on a deep learning extended dynamic mode decomposition algorithm, approximates the Koopman operator, capturing a patient's brain dynamics and forecasting stimulation efficacy. The MPC uses these predictions to converge rapidly to an optimal set of stimulation parameters. The system is capable of adapting to changes in brain dynamics over time, ensuring continuous optimization. Both the predictive model and MPC are implemented in software and on hardware (FPGA and ASIC synthesis), achieving a power consumption of 97.09 μW. Additionally, we present a framework incorporating a neural mass model (NMM) fine-tuned to patient pre-recorded data. The NMM generates synthetic intracranial electroencephalography (iEEG) highly correlated with real iEEG during normal and seizure periods. This framework is used to test and validate other patient-optimized, temporally-adaptive stimulation approaches. Our temporally-adaptive stimulation optimization for seizure control was validated using this framework.
Open Access
AI-Assisted Pipeline for 3D Face Avatar Generation
(2024-11-07) Fadaeinejad, Amin; Troje, Nikolaus
Filling virtual environments with realistic-looking avatars is essential for games, film production, and virtual reality. Creating a fun and engaging experience requires a wide variety of different-looking avatars. There are two main methods to create realistic-looking avatars. One is to scan a real person's face using a light room. The second is for the artist/designer to create the avatar manually using advanced tools. Both of these approaches are expensive in terms of time, computing, and human labour. This thesis leverages generative models like Generative Adversarial Networks (GANs) and Variational Auto-Encoders (VAEs) to automate avatar creation. Our pipeline offers control over three aspects: face shape, skin color, and fine details like beards or wrinkles. This provides artists flexibility in avatar creation and can integrate with tools like MOSAR for controlling avatars from 2D images.
Open Access
DMBench: Load Testing and Benchmarking Tool for Data Migration
(2024-11-07) Hamouda, Fares; Fokaefs, Marios
Data migration involves transferring data between systems, whether homogeneous or heterogeneous, and potentially reformatting it. With large volumes of data, resource constraints, and diverse data models and formats, migration can be critical for enterprises due to time consumption, high costs, and significant risks if not done correctly. Accurate prediction and planning for these challenges are essential for effective migration. This work introduces load testing and benchmarking for data migration to enhance decision-making efficiency and effectiveness. Our framework emphasizes extensibility and customizability for a broader range of tests. We present a prototype architecture, a development roadmap, and a case study demonstrating practical use.
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Piecewise Planar Image Restoration via Gradient Graph Laplacian Regularizer
(2024-11-07) Gharedaghi, Yeganeh; Cheung, Gene
Images in various practical applications often exhibit Piecewise Planar (PWP) characteristics. Common issues such as noise, blur, and distortions arise from sensor limitations, transmission errors, and environmental factors like lighting and sensor quality. Despite advancements in image restoration, significant challenges remain under conditions of poor lighting, camera movement, and occlusions, making PWP image restoration a critical research area. This thesis investigates the restoration of PWP images, focusing on efficiently overcoming these challenges and enhancing image quality in two real-world applications: (i) depth image denoising and (ii) low-light image contrast enhancement. We formulate quadratic objectives regularized by Graph Laplacian Regularizer and Gradient Graph Laplacian Regularizer, tailored for these two applications. These objectives can be efficiently solved in linear time using a conjugate gradient solver and the alternating direction method of multipliers. Experimental results demonstrate that our algorithm achieves competitive image quality while significantly reducing computational complexity.
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Simulation and Analysis of Co-Pt Based Granular Magnetic Recording Media Near the Curie Temperature
(2024-07-18) Kammula, Aaron; Pisana, Simone
Heat-assisted magnetic recording (HAMR) has been shown to satisfy modern data storage of hard disk drives by enabling the writing of high-anisotropy materials such as Fe-Pt. Modelling the magnetization dynamics near the Curie temperature (Tc) is crucial when predicting the limits of HAMR or any technology that relies on fast, high temperature magnetization control. However, there are some differences in how magnetic grains behave under conditions important to HAMR, such as cooling rate and magnetic field angle. Such differences arise from how thermal fluctuations are modeled in the stochastic Landau-Lifshitz-Gilbert equation. Therefore, it is important to benchmark these results against experiments to validate theoretical models. Here, we model the switching probability of granular Co-Pt media with VAMPIRE’s atomistic simulator as preparation for experiments. Unlike Fe-Pt, Co-Pt thin-films serve as a good benchmark due to their tunable composition, allowing us to generously change parameters like saturation magnetization, anisotropy and Tc.
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A First Look at Fairness of Machine Learning Based Code Reviewer Recommendation
(2024-07-18) Mohajer, Mohammad Mahdi; Wang, Song
The fairness of machine learning (ML) approaches is critical to the reliability of modern artificial intelligence systems. Despite extensive study on this topic, the fairness of ML models in the software engineering (SE) domain has yet to be well explored. As a result, many ML-powered software systems, particularly those utilized in the software engineering community, continue to be prone to fairness issues. Taking one of the typical SE tasks, i.e., code reviewer recommendation, as a subject, this work conducts the first study toward investigating the fairness of ML applications in the SE domain, explicitly focusing on the code reviewer recommendation task. Our empirical study demonstrates that current state-of-the-art ML-based code reviewer recommendation techniques exhibit unfairness and discriminating behaviors. Specifically, male reviewers get, on average, 7.25% more recommendations than female code reviewers compared to their distribution in the reviewer set. This work also discusses why the studied ML-based code reviewer recommendation systems are unfair and provides solutions to mitigate the unfairness. For instance, these techniques may recommend male reviewers at a significantly higher rate than female reviewers in a discriminatory manner. Our study further indicates that existing mitigation methods can significantly enhance fairness in projects with a similar distribution of protected and privileged groups. Still, their effectiveness in improving fairness on imbalanced or skewed data is limited. Eventually, we suggest a solution to overcome the drawbacks of existing mitigation techniques and tackle bias in imbalanced or skewed datasets.
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Laser-Induced Graphene Electrodes for Organic Electrochemical Transistors (OECTs)
(2024-03-16) Nazeri, Mohammad; Grau, Gerd
Organic electrochemical transistors (OECTs) have drawn a lot of interest because of their low cost, biocompatibility, and ease of fabrication, allowing them to be utilized in various applications including flexible displays, electrochemical sensing, and biosensing. Key components of OECTs are the gate, source, and drain electrodes. Here, OECTs with laser-induced graphene (LIG) electrodes are presented. The electrode patterns for the source, drain, and gate are created by lasing the polymer substrate polyimide (PI). The entire process is simple and inexpensive without complicated chemical synthesis routines or expensive materials such as gold. Patterns can be customized quickly and digitally. Different laser parameters play an important role in changing the conductivity and porosity of the graphene leading to its use in different applications. The low-cost and porous LIG electrodes with low contact resistance, good electrical stability, and adhesion to the polymeric substrate play an essential role in device performance. Due to the flexibility of the laser process, source, drain, and gate can potentially have different properties even though they are fabricated together in a co-planar architecture. The minimum sheet resistance achieved with this laser method for the square patterned electrodes is 7.86 Ω/sq. The LIG-based OECTs demonstrate good electrical modulation and high on-current. The LIG-based OECT shows low OFF current in the order of 0.035 mA.
Open Access
Modeling and Analysis of Modular Multi-level Converter-Based HVDC Systems to Investigate Geomagnetic Disturbance Effects.
(2024-03-16) Hosseinpour, Hamzeh; Rezaei Zare, Afshin
The MMC-based HVDC systems provide excellent performance. As geomagnetic disturbances (GMD) can be catastrophic events for the power system including MMCs, this research is dedicated to investigating the GMD impacts on the MMC systems. A practical system is selected to provide the most realistic and reliable results. An analytical model is provided to determine the share of the MMC in the harmonics resulting from transformer saturation. Also, a mitigating control block is designed and proposed to eliminate major harmonics in the grid current in the case of GMD. It improves the power quality in the grid, and it reduces the risk of blackout in the system. A control approach is proposed for the reactive power compensation during GMD. It supports grid voltage stability. Simulation results are provided to validate the effectiveness of the proposed methods, which improve the grid power quality and reduce the risk of voltage collapse.
Open Access
An Explainable Knowledge Graph Based Machine Learning Model for Fact Checking
(2024-03-16) Kundu, Arghya; Nguyen, Uyen T.
Misinformation is a growing threat to the economy, social stability, public health, democracy, and national security. One of the most effective methods to combat misinformation is fact checking. In this thesis, we propose fact checking methods using NLP and misinformation propagation patterns. The contributions are, A KG-based fact checking model that uses two separate KGs, one containing true claims and the other, false claims. Additionally, we employ XAI techniques to provide explanations for the model's classification, increasing transparency and user trust. A propagation-based classifier to complement the above KG-based fact checking model for misinformation detection on Twitter. This model uses temporal, spatial and "infectiousness" properties of misinformation. A translator program that converts text with slang and non-standard words (SNSW) into standard English for fact checking on Reddit. The translated content is then input into the above KG-based fact checking model, increasing the model's accuracy.
Open Access
Improving the Motion Processing Hierarchy for Attending to Visual Motion
(2024-03-16) Zhang, Xiao Lei; Tsotsos, John K.
Visual motion has been studied for decades now. Attention to motion using Selective Tuning involves a top-down selection mechanism within a feed-forward motion hierarchy. Researchers have proposed various models for the motion hierarchy. In this thesis, we introduce a learnable hierarchy, based on fully convolutional networks, ST-Motion-Net. The Selective Tuning model for visual attention is demonstrated on ST-Motion-Net to localize motion patterns and segment moving objects. We create two datasets, Blender-MP and Blender-Complex, to evaluate ST-Motion-Net on motion pattern detection, localization, and motion segmentation tasks. ST-Motion-Net achieves excellent performance on motion pattern detection and localization for each area of ST-Motion-Net. For motion segmentation, we evaluate 2-Frame-Area-V1 of ST-Motion-Net on the task. 2-Frame-V1 contains neurons that respond to translation motion, given 2 most recent frames of a temporal sequence. 2-Frame-V1 achieves 86.84% IoU on Blender-MP-Test, which surpass some state-of-the-art models. On Blender-Complex-Test, 2-Frame-V1 reaches 52.61% IoU, which also achieves state-of-the-art performance.
Open Access
Speech Emotion Recognition in Conversations Using Graph Convolutional Networks
(2024-03-16) Chandola, Deeksha; Jenkin, Michael R.
Speech emotion recognition (SER) is the task of automatically recognizing emotions expressed in spoken language. Current approaches focus on analyzing isolated speech segments to identify a speaker’s emotional state. That being said, models based on text-based emotion recognition methods are considering conversational context and are moving towards emotion recognition in conversation (ERC). With the availability of multimodal datasets, ERC can be extended to non-text modalities as well. Building on these advances, in this thesis, we propose SERC-GCN, a method for speech emotion recognition in conversation (SERC) that predicts a speaker’s emotional state by incorporating conversational context, specifically speaker interactions, and temporal dependencies between utterances. SERC-GCN is a two-stage method. In the first stage, emotional features of utterance-level speech signals are extracted using a graph-based neural network. Here each individual speech utterance is transformed into a cyclic graph. These graphs are then processed by a two layered GCN architecture followed by a pooling layer to extract utterance-specific emotional features. In the second stage, these features are used to form conversation graphs that are used to train a graph convolutional network to perform SERC. We empirically evaluate the effectiveness of SERC-GCN on two benchmark dataset; IEMOCAP and MELD. Results show that SERC-GCN outperforms existing baseline approaches on these datasets.
Open Access
Extreme In-Plane Thermal Conductivity Anisotropy in Rhenium-Based Dichalcogenides
(2023-12-08) Tahbaz, Sina; Pisana, Simone
Anisotropies in thermal conductivity are important for thermal management in a variety of applications, but also provide insight on the physics of nanoscale heat transfer. As materials are discovered with more extreme transport properties, it is interesting to ask what the limits are for how dissimilar the thermal conductivity can be along different directions in a crystal. In this thesis the thermal properties of Rhenium-based transition metal dichalcogenides (TMDs), specifically Rhenium Disulfide (ReS2) and Rhenium Diselenide (ReSe2) are reported, highlighting their extraordinary thermal conductivity anisotropy. Along the basal crystal plane of ReS2, a maximum of 169 ± 11 W/mK is detected along the b-axis and a minimum of 53 ± 4 W/mK perpendicular to it. For ReSe2, the maximum and minimum values of 116 ± 3 W/mK and 27 ± 1 W/mK are found to lie 60◦ and 150◦ away from the b-axis, along the polarization direction of some of the principal Raman modes. These measurements demonstrate a remarkable anisotropy of 3.2× and 4.3× in the conductivity within the crystal basal planes, respectively. The through-plane thermal conductivities, recorded at 0.66 ± 0.01 W/mK for ReS2 and 2.31 ± 0.01 W/mK for ReSe2, highlight the impact of their layered structures, contributing to notably high in-plane to through-plane thermal conductivity ratios of 256× for ReS2 and 50× for ReSe2. This research demonstrates the unique thermal properties that these comparatively underexplored TMDs have, shedding light on the need for further exploration into the intricate thermal behavior of such materials, while underscoring their potential significance for future applications in the fields of semiconductor devices and nanotechnology.
Open Access
Efficient Techniques for Automated Planning for Goals in Linear Temporal Logics on Finite Traces
(2023-12-08) Fuggitti, Francesco; Giacomo, Giuseppe De; Lespérance, Yves
One of the greatest challenges of the modern era is to empower AI systems with the ability to deliberate and act autonomously while mitigating the risks that arise from granting such power. To address this challenge, a promising approach is to incorporate behavioral specifications within AI systems using formal languages, especially linear temporal logics. We are interested in efficiently combining temporal logics on finite traces with automated planning, which is an AI model-based approach to producing autonomous behavior and solving the problem of sequential decision-making. Despite the ample literature on the application of linear temporal logics on finite traces, LTLf and LDLf, in planning and related fields, limited attention has been given to the study and use of the pure-past linear temporal logics and their potential for specifying temporal goals in planning. Furthermore, the application of temporal logics to other related research areas where planning techniques have been successfully employed, such as business process management and business automation, has been given relatively little focus, and there is a lack of principled research on the topic. In this dissertation, we propose (i) an in-depth study of the pure-past linear temporal logics, (ii) their effective applicability as formal languages to specify temporally extended goals in deterministic and nondeterministic planning, and (iii) the application of planning techniques to solve the declarative trace alignment in business process management while envisioning new methods to solve workflow construction from natural language in business automation. More specifically, we first review the pure-past linear temporal logics, PPLTL and PPLDL, and we show how we can exploit a foundational result on reverse languages to get an exponential improvement over LTLf/LDLf, when computing the corresponding deterministic automata. Given this key result, we introduce an efficient technique to cleverly evaluate the truth of pure-past formulas given the truth value of a small set of subformulas, thus enabling the development of more efficient algorithms. Consequently, in the context of deterministic and nondeterministic planning for pure-past temporally extended goals, we present a novel efficient encoding into standard planning for final-state goals with minimal overhead, and that is at most linear in the size of the goal formula and does not add additional spurious actions. As for declarative trace alignment, we extend process model specifications to full LTLf/LDLf, provide a reduction to cost-optimal planning, and devise new practical encodings. Finally, focusing on the enterprise use of business automation, we look into the latest techniques in natural language understanding and large language models to translate English instructions to LTL formulas, bridging the gap between the end user and reasoning engines used to construct automatic workflows.
Open Access
Predicting Retinal Ganglion Cell Responses Based on Visual Features via Graph Filters
(2023-12-08) Parhizkar, Yasaman; Eckford, Andrew W.
This thesis presents a novel graph-based approach to classify video clips with binary labels. Each video clip is described by a feature vector instead of raw pixel values. At the model's core, a similarity graph is defined where each node is associated with a feature vector and its corresponding label. The weight of an edge connecting two nodes delineates the similarity of the nodes' feature vectors which is computed via the Mahalanobis distance and its metric matrix. The metric matrix is learned using labeled training data. Unknown labels are then estimated using the optimized metric and the similarity graph. The main advantage of our model is enabling interpretations of how the predictions are made. Nevertheless, the model achieves competitive accuracy with state-of-the-art approaches as well. We apply this model to a retinal coding problem where explainability is essential to gain conceptual insight about the retina.
Open Access
Evaluating Blockchain Networks through Real-Time Simulation
(2023-12-08) Saniee Monfared, Sahand; Liaskos, Sotirios
The massive scale and distributed nature of Internet-of-Things (IoT) presents challenges in realizing practical and effective security solutions. Blockchain-empowered platforms and technologies have been proposed to address aspects of this challenge. In order to realize a practical Blockchain deployment for IoT, there is a need for a testing and evaluation platform for the performance and security of Blockchain applications and systems. This research involves conducting a series of experiments through real-time simulation of the Algorand blockchain platform to assess its efficiency across different configurations. These experiments provide valuable insights into the behavior of the Algorand blockchain platform. The findings contribute to a better understanding of the platform's performance, and resource utilization under different conditions.