Electrical and Computer Engineering

Permanent URI for this collectionhttps://hdl.handle.net/10315/35603

Browse

Now showing 1 - 20 of 86

Access status: Open Access ,
Deep Learning-Based Detection, Annotation and Staging of Breast Cancer Metastasis on Whole-Slide Histopathology Images of Lymph Nodes
(2025-07-23) Tauqeer, Abdullah; Sadeghi-Naini, Ali
Breast cancer is one of the most frequently diagnosed malignancies and a leading cause of cancer-related mortality among women worldwide. Accurate detection and characterization of breast cancer lymph node metastasis are crucial for effective patient management. In this thesis, two novel deep learning frameworks have been developed and investigated to automate the analysis of hematoxylin and eosin (H&E) stained digital pathology whole-slide images (WSIs) of lymph nodes to streamline the breast cancer pathology workflow in the clinic. First, a nuclei segmentation and classification model, TexSegNet, is introduced to extract detailed cellular-level information from the digital pathology images of biopsied lymph nodes. This model has been trained and evaluated using 7,904 histology images from the PanNuke dataset, which includes 205,343 annotated nuclei across 19 tissue types (2,351 images from breast tissue). TexSegNet leverages a hybrid encoder-decoder architecture, integrating multi-scale convolutions, nuclear texture extraction blocks, advanced attention mechanisms, and a feedback-driven classification branch. Trained on all tissue types of the PanNuke dataset and subsequently fine-tuned on its breast subset, TexSegNet has achieved an overall accuracy of 81.4±0.4% in detecting and classifying nuclei on the breast test set, substantially outperforming established benchmarks such as HoVer-Net, CellViT, and StarDist. Notably, TexSegNet has maintained consistently good performance across various cell types, including underrepresented ones, with F1-scores of 89.3±0.4%, 91.1±0.5%, 88.9±0.8%, and 84.3±0.3% in detecting and classifying neoplastic, epithelial, inflammatory, and connective cell nuclei, respectively. Second, a selective neighborhood attention-based multiple instance learning framework (MIL) is proposed for automated detection, localization, and staging of lymph node metastases in breast cancer. The proposed framework leverages a dual-path feature extractor, incorporating both nuclei segmentation/classification outputs and transformer-based tissue features, alongside a dynamic attention mechanism that selects and emphasizes neighboring patches based on similarity to the target patch. Trained and optimized on 269 WSIs (over 14 million image patches) from the CAMELYON16 dataset and evaluated on an independent test set of 129 WSIs, the model achieves 96.2±1.5% sensitivity, 95.3±2.4% precision, and 95.7±3.1% F1-score in patch-level classification, along with the area under the receiver operating characteristic curve (AUC) of 0.96±0.01 for slide-level classification. External validation on an out-of-distribution (OOD) subset of 30 annotated WSIs from the CAMELYON17 dataset (representing multiple institutions) demonstrates robust generalizability, with a patch-level F1-score of 87.0±1.8% and a slide-level AUC of 0.88±0.03. Furthermore, for 500 WSIs from 100 patients in the CAMELYON17 dataset, the framework demonstrates an excellent performance in patient-level pN-staging, with a quadratic-weighted Cohen’s kappa of 0.94±0.02, indicating near-expert concordance in detecting and classifying the extent of nodal metastasis. Ablation analyses underscore the importance of incorporating nuclei-based features and selective neighborhood attention, with noticeable performance degradation observed when either element is removed. By integrating insights into histologic type, morphology and spatial heterogeneity at the cellular level with contextual information at the tissue level, the proposed framework effectively replicates key aspects of human pathological assessment and shows promise as a decision-support tool for metastatic breast cancer detection, annotation, and staging in the era of digital pathology. Together, these two frameworks, TexSegNet for detailed nuclei segmentation and classification, and the selective neighborhood attention-based MIL model for targeting metastatic tumor detection and staging, form an integrated pipeline for breast cancer pathology assessment on standard H&E-stained slides. Together, they demonstrate strong potential to reduce pathologist workload, reduce subjectivity in diagnosis, and serve as processing tools in digital pathology to derive quantitative biomarkers for further analysis or prognostication. Future work will focus on expanding dataset diversity, enhancing model explainability, and integrating these algorithms into clinical workflows to support precision oncology care.
Access status: Open Access ,
Transfer Learning for Data-Driven Power Flow and Optimal Power Flow Applications
(2025-07-23) Nazari, Dorsa; Srikantha, Pirathayini
Ensuring real-time grid operations is essential for maintaining both stability and efficiency in today’s dynamic power systems. While machine learning (ML)-based approaches enable fast inference, these models are often trained using datasets derived from static grid configurations, such as fixed topologies. Adapting these models to evolving grid conditions introduces additional complexity and necessitates acquiring supplementary datasets, which require computationally intensive solvers. This paper presents a method that improves computational efficiency compared to conventional transfer learning techniques for adapting ML models used in power flow (PF) and optimal power flow (OPF) analysis to changing grid conditions. Our findings indicate that only 6% of the original dataset requires recalibration, and the entire process of data point regeneration and model fine-tuning is completed in under 3 seconds in the benchmark IEEE 14-bus, IEEE 118-bus, and PEGASE 1352-bus systems.
Access status: Open Access ,
Field Testing and Economic Analysis of Residential Vehicle to Grid Deployment
(2025-07-23) Nasr, Khunsha; Farag, Hany E. Z.
The development of Electric Vehicles (EVs) has surged in recent years, positioning them as direct replacements for fossil fuel-dependent internal combustion engines. However, the rise in EVs will place considerable strain on the grid, necessitating transmission and distribution infrastructure upgrades. In this regard, EVs equipped with bidirectional charging can act as independent energy storage, managing energy at home during normal and emergency conditions, feeding excess energy back into the grid to reduce its strain, and potentially generating revenue for their owners. However, the high cost of Residential Bidirectional Chargers (RBC) and the limited availability of EVs that support bidirectional charging, particularly Vehicle-to-Home/Grid (V2H/G), remain significant barriers. Despite extensive theoretical research on bidirectional charging of EVs, there is a lack of real-world testing and comprehensive techno-economic analysis to assess the feasibility of the wide deployment of these technologies in residential areas. This thesis aims to investigate the techno-economic viability for the wide deployment of RBC throughout the following: 1. Field Testing for RBCs: Conducting practical evaluations of RBCs to gather empirical data on their performance, efficiency, and reliability under real-world conditions. 2. Identification of Policy and Regulatory Barriers: Analyzing the legislative and regulatory frameworks in Ontario to identify obstacles that hinder the deployment of RBCs, and proposing solutions to overcome these barriers. 3. Development of a Mathematical Model: Creating a model to perform a regional-wide cost-benefit analysis for RBC deployment programs, considering three main stakeholders: local distribution companies, EV owners, and ratepayers. By addressing these aspects, this research will provide a comprehensive understanding of the practical and economic implications of implementing RBC technologies, paving the way for its broader adoption and integration into the energy infrastructure.
Access status: 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.
Access status: 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.
Access status: 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.
Access status: 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.
Access status: 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.
Access status: 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.
Access status: 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.
Access status: 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.
Access status: Open Access ,
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.
Access status: 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.
Access status: 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.
Access status: 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.
Access status: 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.
Access status: 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.
Access status: 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.
Access status: 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.
Access status: 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.

Browse

Recent Submissions