Electrical and Computer Engineering

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

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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
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.
Open Access
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.
Open Access
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.
Open Access
How effective can we detect software vulnerabilities using code clones? - A Case Study on Ethereum Smart Contracts
(2023-12-08) Ma, Yinghang; Jiang, ZhenMing
Smart contracts are self-executing programs that are deployed on blockchain platforms to provide services and handle transactions. Solidity contracts exhibit different code characteristics compared to software projects written in conventional programming languages and have a much higher level of code-to-clone ratio. These differences can impose a wider spread of security risks, and cloned code snippets may suffer from the same security problems as their cloned counterpart. In this thesis, we have conducted an empirical study on the effectiveness of leveraging code detection techniques to identify software vulnerabilities in the Solidity contract code. We have experimented with a set of configuration tuning approaches while keeping everything else constant. After carefully tuning these configurations, the tools tuned under the context-specific tuning approaches can achieve significant improvement while detecting vulnerabilities. This thesis highlighted the need for further research into context-specific clone detection and management and motivating studies in the domain of blockchain-based applications.
Open Access
Ownership and Accountability in Software Teams
(2023-12-08) Koana, Umme Ayman; Nayebi, Maleknaz
Ownership of software artifacts has become a point of interest to software teams. Researchers modeled ownership of software artifacts with different models and in relationship with a variety of code and developers’ performance metrics. These models have been evaluated for both propriety and open-source software. Bightsquid is a software startup that provides a healthcare communication system. At the time of the COVID-19 pandemic (starting March 2020), the company actively modified its processes to improve developers’ experience and accountability. As the provider of health communications, Brighsquid was receiving an amplified number of user requests to accommodate the changing needs in the health care system. Yet, the management team observed the lack of accountability among the developers in accepting and finalizing these requests. The company changes the task assignment process to the team with the main motivation to increase developers’ accountability. Motivated by this problem statement and the status of the partnered company, this thesis presents four main contributions: a systematic literature review on software ownership, a case study with Brighsquid to compare their ownership status with existing research, an evaluation of the impact of enhanced accountability through a comparative analysis of issue assignment models, and a survey of software developers to explore the broader relationship between accountability and ownership.
Open Access
Printed Tattoo Electrodes for Electrophysiological Signal Acquisition
(2023-12-08) El-Hajj, Yoland Jamal; Grau, Gerd
The enhancement of medical tattoo electrodes over the past few decades have enabled them to serve as an alternative to conventional Ag/AgCl electrodes. Additive manufacturing methods can enable efficient and flexible fabrication of the tattoo electrodes, in contrast to the traditional fabrication methods demonstrated thus far. The objective of this project is to optimize printing methods to fabricate medical tattoo electrodes, as well as analyze and compare their performance both in vitro and in vivo to Ag/AgCl electrodes. Inkjet and extrusion printing methods were optimized to print various electrode patterns on a tattoo paper substrate and a simple contact platform was developed to allow for external connections to rigid components. Initial testing and analysis of the electrical and mechanical performance of the electrodes outside the human body was subsequently performed. The electrodes were ultimately tested on human subjects to acquire ECG and EMG signals, which were analyzed in terms of signal quality.
Open Access
Manufacturing Strain Sensor Via Printed Electronics Onto 3D Printed Substrates
(2023-12-08) Badrian, Babak; Grau, Gerd
Inkjet printing is a promising technology with advantages such as digital customization, reduced cost and fabrication time, and non-contact printing. These features enable inkjet printing to fabricate on novel, diverse substrates such as 3D printed substrates. 3D printing technology builds 3D structures with freedom of design, mass customization, low cost, and ability to fabricate complex geometries. In this thesis, we aim to integrate inkjet printing with 3D printing technology and fabricate a strain gauge sensor. We use various additive manufacturing (AM) techniques including 3D fused filament fabrication (FFF) printing, extrusion printing, and inkjet printing. We used silver nanoparticle ink and studied different conditions to maximize electrical conductivity of the ink. This optimization includes ultraviolet (UV) time exposure, drop spacing, heating conditions, intense pulsed light parameters, and length shrinkage. A low-cost strain gauge sensor is fabricated onto 3D printed PLA material. The related mechanical/electrical tests are performed for the sensor characterization.
Open Access
Extending the Range of Depth Cameras using Linear Perspective for Mobile Robot Applications
(2023-03) Naheyan, Tasneem; Elder, James
Reliable depth sensing is essential in robotics for both basic and advanced robot operations. Depth cameras capture depth data that can be used by a robot’s vision system, but the quality of the data is limited. Many depth estimation and completion algorithms have been introduced to process camera data and predict depth in a scene, but extending camera range is a little explored problem. This thesis presents a geometry-based method that applies a Manhattan constraint and regresses onto sparse depth input to interpolate and extrapolate lines in the scene in order to extend range. To evaluate the proposed approach, a long-range RGBD dataset with corresponding LiDAR ground truth is presented. Experiments demonstrate that the proposed method successfully interpolates and extrapolates detected 3D lines in Manhattan scenes given sparse depth data within a few centimeters of error, providing depth information in parts of the scene missing input depth data from the sensor. The proposed approach performs comparably to a baseline method in interpolating depth and outperforms it in extrapolation.
Open Access
Modelling the Relationship Between Physiological Measures of Motion Sickness
(2023-08-04) Shodipe, Oluwaseyi Elizabeth; Allison, Robert
Car sickness is anticipated to occur more frequently in self-driving vehicles because of their design. This thesis involved an investigation using machine learning techniques with physiological measures to detect and predict the severity of car sickness in real-time every two minutes. A total of 40 adults were exposed to two conditions, each involving a 20-minute ride on a motion-base simulator. Car sickness incidence and severity were subjectively measured using the Fast Motion Sickness (FMS) and Simulator Sickness Questionnaire (SSQ). Car sickness symptom was successfully elicited in 31 participants (77.5%) while avoiding simulator sickness. Results showed that head movement had the strongest relationship with car sickness, and there was a moderate correlation between heart rate and skin conductance. The machine learning models revealed a medium correlation between the physiological measures and the FMS scores. An acceptable classification score distinguishing between motion-sick and non-motion-sick participants was found using the random forest model.
Open Access
Depth Perception Under Scaled Motion Parallax in Virtual Reality
(2023-08-04) Teng. Xue; Allison, Robert
This thesis investigates the impact of mismatch between virtual and physical motion on the perception of object shape. We varied the gain between virtual and physical head motion and measured the effect on depth, distance and shape perception. Our results showed that under monocular viewing, both depth and distance settings decreased with increasing gain, especially at close distances. The average effect sizes of gain were up to -0.061 m/gain unit and -0.40 m/gain unit on depth and distance, respectively, when measured on a standard fold with depth of 1 m. Observers experienced less distortions than predicted from a geometric model and very little depth distortion (not statistically significant effect of gain) under binocular viewing. The distance distortion caused by gain was reduced by up to 56.6% compared to monocular viewing. Binocular cues to depth and distance and large distance (at 6 m) enhance humans’ tolerance to visual and kinesthetic mismatch.