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Steering behavior-based fatigue detection: Evaluation and implementation for drowsy driver warning system

(2025) Hassan Ananda Kumar, Sanjana

This research was conducted to analyze steering behavior as an alternative approach to detecting driver drowsiness. The thesis examined standard metrics such as steering wheel angle, steering wheel angle rate, yaw rate, and lane position—variables directly and indirectly related to steering behavior—to assess how much information they bear about drowsiness. A derived metric, the steering reversal rate, was also analyzed to further explain the effects of drowsiness. These metrics demonstrated a strong correlation with driver drowsiness, which was subjectively measured using the Karolinska Sleepiness Scale (KSS). Based on this analysis, two parameters derived from the steering reversal rate—micro and macro corrections—were used to develop two methods for detecting drowsiness. These parameters were significant because, as a driver becomes drowsier, the frequency of micro-corrections tends to decrease, while macro-corrections increase. Two methods have been developed to detect a drowsy driver based on the above analysis. The first method employed a logistic regression model, using the absolute values of micro and macro corrections to directly correlate with the KSS ratings. This approach did not account for temporal patterns and treated the data independently of its time-series nature. In contrast, the second method incorporated a time-series perspective by evaluating changes in micro and macro correction rates over time rather than relying on their absolute values. During development, it was observed that vehicle speed significantly influenced steering behavior. At lower speeds, even non-drowsy drivers exhibited more macro corrections and fewer microcorrections. However, at speeds above 65 km/h, non-drowsy drivers typically made more micro-corrections and fewer macro-corrections. This insight enhanced the robustness of the second method, wherein vehicle speed was considered one of the contributing factors in analyzing driver steering behavior. Additionally, the second method involved a learning phase for each individual driver, allowing for personalized threshold values. This driver-specific calibration improved adaptability. Overall, the second method of real-time analysis of changes in micro and macro correction rates proved to be a more effective and reliable approach, yielding better results than the current system(based on lane distance) for detecting driver drowsiness.

Enhancing Gas Adsorption In Sensors：Au–Pt Nanoparticles and Methylated Coordination Cages

(2025) Advand, Marzyeh

This thesis investigates two nanostructured materials, bimetallic gold platinum (AuPt) nanoparticles and a methylated cobalt(II)/iron(II) coordination cage, for their potential use in gas sensors designed to detect acetone, a key biomarker for non-invasive glucose monitoring. The AuPt nanoparticles were synthesized using a modified co-reduction method based on Britto’s procedure. The synthesis was optimized to achieve uniform and well-dispersed nanoparticles through precise control of reaction time and washing steps. The nanoparticles were characterized using SEM and, EDX, to confirm their morphology, composition, and crystal structure. They were immobilized on quartz substrates through silane functionalization, and UV–Vis spectroscopy was used to verify both immobilization and acetone vapor adsorption. A clear spectral shift observed after acetone exposure confirmed successful adsorption on the nanoparticle surface, indicating their suitability for gas sensing applications. In parallel, methylated Co(II) and Fe(II) coordination cages were synthesized through subcomponent self-assembly and characterized using NMR and UV–Vis spectroscopy. The immobilization of Co(II) cages on glass and silicon substrates was verified using UV–Vis and TOF-SIMS. The data is consistent with successful immobilization of the cages on both glass and silicon surfaces. Notably, this study demonstrates for the first time that this particular cage has been successfully adsorbed onto glass and silicon substrates. Gas exposure experiments were performed on the Fe(II) cage solutions and analyzed by NMR; however, no significant spectral changes were detected, likely due to gas dissipation or weak interactions at ambient conditions. Overall, the study presents promising results for the AuPt nanoparticles, while preliminary findings for the coordination cages indicate the need for further optimization. These results contribute to the development of nanostructured mate- rials for more sensitive and selective gas-sensing technologies aimed at advancing non-invasive diagnostic methods.

Reproducible Performance Variability Mitigation of OpenMP and SYCL Applications

(2025) Persson, Christoffer; Prétot, Mathias

Performance variability caused by unpredictable system noise remains a persistent challenge in high-performance and parallel computing. This thesis presents a methodology for characterising such variability through reproducible noise injection, using three representative benchmarks implemented with OpenMP and SYCL. A custom noise injector was developed to capture real system traces, isolate average and outlier behaviours, and reinject the delta as controlled, reproducible noise. We evaluate and compare multiple mitigation strategies, such as thread pinning, use of housekeeping cores, and simultaneous multithreading (SMT) toggling, under both default and noise-injected conditions. Our experimental study spans three benchmarks (N-body, Babelstream, and MiniFE) executed on local Intel and AMD desktop processors, enabling a comprehensive analysis of mitigation effectiveness across platforms and workloads. Results indicate that while OpenMP consistently delivers higher raw performance, SYCL tends to be more resilient to noisy environments. The proposed noise injection framework facilitates more rigorous and repeatable assessment of parallel program behaviour under controlled perturbations. Although the effectiveness of mitigation strategies varies with workload characteristics, system configuration, and noise intensity, certain techniques, such as isolating housekeeping cores, show clear benefits, particularly in high-noise scenarios.

ILAN: The Interference- and Locality-Aware NUMA Scheduler

(2025) Carlsson, Axel; Mellberg, Edvin

Non-Uniform Memory Access (NUMA) systems are increasingly common as the goto processor architecture for parallel computing within the field of High-Performance Computing (HPC). Similarly, OpenMP is the de-facto standard runtime for enabling parallelism. However, the default OpenMP runtime does not account for interference or data locality aspects, leading to performance degradations on NUMA systems where these effects become magnified. To address these challenges, this thesis proposes ILAN, an interference- and data locality-aware NUMA scheduler integrated into the LLVM OpenMP runtime, specifically targeting the taskloop construct. ILAN utilizes hardware topology information to enable a more structured task distribution strategy compared to the default OpenMP tasking scheduler, the work stealing scheduler, yielding improved data locality. Furthermore, the ILAN scheduler utilizes moldability to incorporate interference awareness, dynamically reducing the number of OpenMP threads to mitigate the effects of interference while further improving data locality. Performance evaluation using the NAS Parallel Benchmarks, Matrix Multiplication, and LULESH on a multi-socket NUMA platform demonstrates an average speedup of 10%, with a maximum speedup of 46%, compared to the default OpenMP work stealing scheduler.

Large Scale Efficient Data Readout for Vehicle Fleets

(2025) Johnsson, Simon

As vehicles become more technologically advanced, the data generated by a single vehicle reach significant amounts. Diverse data has high potential in use cases such as machine learning by providing insights into different conditions. Currently, there is no clear solution for collecting this data as vehicle systems are restricted in terms of compute, memory, storage, and bandwidth. This thesis investigates the problem of large scale vehicle data readout and presents a solution to it, providing a significant increase by leveraging lossless streaming based compression at low cost. Furthermore, it addresses the architecture necessary in order to sufficiently process the data globally and how best to integrate this efficiently with a massive number of vehicle systems. Lastly, a generalized model is formulated at the micro scale, which establishes the requirements in terms of compute and memory on a single vehicle system based on the findings presented. At the macro scale, the infrastructure required to support the solution is discussed.