172 research outputs found
Cubic III-Nitrides for photonics: Physics, materials, and devices
No full textSubmission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01The student, Jaekwon Lee, accepted the attached license on 2025-03-14 at 13:40.The student, Jaekwon Lee, submitted this Dissertation for approval on 2025-03-14 at 14:08.This Dissertation was approved for publication on 2025-03-18 at 14:57.DSpace SAF Submission Ingestion Package generated from Vireo submission #21678 on 2025-10-19 at 19:52:30Light-emitting diodes (LEDs), especially InGaN-based LED devices, have achieved remarkable success in solid-state lighting, contributing to 25 % of energy savings already. However, expected population growth and increasing demand for lighting necessitate a more efficient approach, which can only be realized by solving the issue of the green gap (i.e., the inefficiency of the state-of-the-art green LEDs). Cubic nitride LEDs are proposed as a promising solution to the green gap, due to well-documented advantages in the literature. However, there are theoretical and experimental issues in the current approach that need to be innovated in order to translate these material advantages into functional devices and address the green gap with the cubic nitride approach. In this thesis, the realization of cubic nitride LEDs will be tackled in both theoretical and experimental aspects. First, three crucial design rules specific to cubic nitride LEDs are suggested, enabling highly efficient theoretical stack design. Second, high-quality, phase-pure cubic GaN is demonstrated and characterized as a template for further material growth. Next, purely cubic active layers are synthesized and characterized, showing high enough internal quantum efficiency with green emission to make a functional device. Finally, the LED and micro-LED fabrication based on cubic GaN template are shown, and the characterization of these novel devices is discussed
Two-dimensional materials for artificial sensory devices: advancing neuromorphic sensing technology
No full textTwo-dimensional materials like graphene and transition metal dichalcogenides offer unique properties ideal for neuromorphic and artificial sensory devices, enabling precise emulation of neural functions with enhanced energy efficiency and flexibility. Despite their potential, challenges such as scalability, uniformity, and stability hinder widespread adoption. This review explores advancements, applications in robotics and AI, and future challenges in scaling 2D-based neuromorphic devices for real-world use.
Expression of iron homeostasis proteins in the spinal cord in experimental autoimmune encephalomyelitis and their implications for iron accumulation
No full text
Galaxy of Atoms
No full textAtoms, the smallest particle constituting a material, are aligned like stars in a semiconductor. Astronomers use telescopes to observe the galaxy; scientists use state-of-the-art microscopy to study the galaxy of atoms: they can observe single atoms smaller than 1/100,000 of your hair diameter. The image shows a particular semiconductor sample named cubic nitride, which shows the highest-efficiency green light emission among the same type thanks to the perfect alignment of atoms. It is measured with a state-of-the-art aberration-corrected scanning transmission electron microscope
WCET and Priority Assignment Analysis of Real-Time Systems using Search and Machine Learning
No full textReal-time systems have become indispensable for human life as they are used in numerous industries, such as vehicles, medical devices, and satellite systems. These systems are very sensitive to violations of their time constraints (deadlines), which can have catastrophic consequences. To verify whether the systems meet their time constraints, engineers perform schedulability analysis from early stages and throughout development. However, there are challenges in obtaining precise results from schedulability analysis due to estimating the worst-case execution times (WCETs) and assigning optimal priorities to tasks.
Estimating WCET is an important activity at early design stages of real-time systems. Based on such WCET estimates, engineers make design and implementation decisions to ensure that task executions always complete before their specified deadlines. However, in practice, engineers often cannot provide a precise point of WCET estimates and they prefer to provide plausible WCET ranges.
Task priority assignment is an important decision, as it determines the order of task executions and it has a substantial impact on schedulability results. It thus requires finding optimal priority assignments so that tasks not only complete their execution but also maximize the safety margins from their deadlines. Optimal priority values increase the tolerance of real-time systems to unexpected overheads in task executions so that they can still meet their deadlines. However, it is a hard problem to find optimal priority assignments because their evaluation relies on uncertain WCET values and complex engineering constraints must be accounted for.
This dissertation proposes three approaches to estimate WCET and assign optimal priorities at design stages. Combining a genetic algorithm and logistic regression, we first suggest an automatic approach to infer safe WCET ranges with a probabilistic guarantee based on the worst-case scheduling scenarios. We then introduce an extended approach to account for weakly hard real-time systems with an industrial schedule simulator. We evaluate our approaches by applying them to industrial systems from different domains and several synthetic systems. The results suggest that they are possible to estimate probabilistic safe WCET ranges efficiently and accurately so the deadline constraints are likely to be satisfied with a high degree of confidence. Moreover, we propose an automated technique that aims to identify the best possible priority assignments in real-time systems. The approach deals with multiple objectives regarding safety margins and engineering constraints using a coevolutionary algorithm. Evaluation with synthetic and industrial systems shows that the approach significantly outperforms both a baseline approach and solutions defined by practitioners. All the solutions in this dissertation scale to complex industrial systems for offline analysis within an acceptable time, i.e., at most 27 hours
Probabilistic Safe WCET Estimation for Weakly Hard Real-Time Systems at Design Stages
No full textWeakly hard real-time systems can, to some degree, tolerate deadline misses, but their schedulability still needs to be analyzed to ensure their quality of service. Such analysis usually occurs at early design stages to provide implementation guidelines to engineers so that they can make better design decisions. Estimating worst-case execution times (WCET) is a key input to schedulability analysis. However, early on during system design, estimating WCET values is challenging and engineers usually determine them as plausible ranges based on their domain knowledge. Our approach aims at finding restricted, safe WCET sub-ranges given a set of ranges initially estimated by experts in the context of weakly hard real-time systems. To this end, we leverage (1) multi-objective search aiming at maximizing the violation of weakly hard constraints in order to find worst-case scheduling scenarios and (2) polynomial logistic regression to infer safe WCET ranges with a probabilistic interpretation. We evaluated our approach by applying it to an industrial system in the satellite domain and several realistic synthetic systems. The results indicate that our approach significantly outperforms a baseline relying on random search without learning, and estimates safe WCET ranges with a high degree of confidence in practical time (< 23h).</p
Estimating Safe Worst-Case Execution Times of Real-Time Systems Using Search and Machine Learning
No full textThis page contains the artefacts of our paper, entitled "Estimating Safe Worst-Case Execution Times of Real-Time Systems Using Search and Machine Learning".OverviewEstimating worst-case execution times (WCET) is an important activity at both early design and late development stages of real-time systems. Based on the WCET estimates, engineers make design and implementation decisions to ensure that task executions always complete before their specified deadlines. However, in practice, engineers often cannot provide precise point WCET estimates and prefer to provide plausible WCET ranges. Given a set of real-time tasks with such ranges, we provide an automated technique to determine for what WCET values the system is likely to meet its deadlines, and hence operate safely. Our approach combines a search algorithm for generating worst-case scheduling scenarios with polynomial logistic regression for inferring safe WCET ranges. We evaluated our approach by applying it to three industrial systems from different domains. Our approach efficiently and accurately estimates safe WCET ranges within which deadlines are likely to be satisfied with high confidence.PrerequisiteSAFE runs on the following operating systems:- CentOS Linux operating system, version 7- MacOS 10.15.7SAFE requires the following tools:- Java 1.8.0.241- R 3.6.2 or above (required libraries: MASS, dplyr, MLmetrics, randomForest, nloptr, stringr, cubature, effsize, scales, ggplot2, grid, gridExtra)- Python 3.6.8 or above (required libraries: tqdm)Input file:* Step 0: Extract SAFE.zip to any PATH* Step 1: See PATH/res/industrial/ADCS.csvHow to run SAFE?* Step 0: Extract SAFE.zip to any PATH* Step 1: Move to PATH and run ./run_safe.sh* Step 2: See output files in ./results/ADCS_SAFEHow to run experiments?Note: Due to randomness of SAFE, we repeat our experiments 50 times=EXP1=* Step 0: Extract SAFE.zip to any PATH* Step 1: Move to PATH and run ./run_exp1.sh* Step 2: See output files in ./results/EXP1/ADCS_SAFE and ./results/EXP1/ADCS_Baseline=EXP2=* Step 0: Extract SAFE.zip to any PATH* Step 1: Move to PATH and run ./run_exp2.sh* Step 2: See output files in ./results/EXP2/ADCS_SAFE/_dist and ./results/EXP2/ADCS_SAFE/_random=EXP3=* Step 0: Extract SAFE.zip to any PATH* Step 1: Move to PATH and run ./run_exp3.sh* Step 2: See output files in ./results/EXP3/ADCS_SAFEFile: SAFE.zip* All scripts and artefacts for our experiments* artefacts folder: Containing Java executable files* res folder: Containing the input task description* SAFE folder: source codes for Java executable files and scripts* scripts folder: Containing Python and R scripts to help the EXPs and generate graphs* run_*.sh files: Shell scripts for executing each EXPs in the paperFile: experiments.zip* Experiment results (readable version)* EXP1 folder: Our experiment results obtained by conducting EXP1* EXP2 folder: Our experiment results obtained by conducting EXP2* EXP3 folder: Our experiment results obtained by conducting EXP3* EXP*-graphs folders: Graphs generated from each EXP * Summary-RQ1.xlsx file: Table 3 in the paperFile: raw_results.zip* Experiment results for all subjects and approaches (raw data)</div
Decentralisation and government trust in South Korea: Distinguishing local government trust from national government trust
No full text- …
