acsl-tcu/common_matlab
May 2025 – Jun 2025
2 Months active
Languages Used
MATLAB
Technical Skills
Control SystemsData ProcessingMachine Learning Data PreparationRoboticsSimulationCode Documentation
YosukeKoseki
PROFILE
Yosukekoseki
Over a two-month period, contributed to the acsl-tcu/common_matlab repository by developing 13 features and resolving 3 bugs, focusing on simulation, machine learning data workflows, and data visualization for drone and robotics applications. Enhanced simulation capabilities by tuning parameters for longer-horizon experiments and integrated machine learning-ready data logging, including circular reference trajectory generation. Improved data preprocessing, modularity, and reproducibility by organizing workflows and incorporating external dataset scripts. Advanced plotting and validation pipelines with new utilities and figure outputs, while establishing test scaffolding and performing code cleanup. Utilized MATLAB scripting, data engineering, and control systems expertise to strengthen reliability and maintainability.
Overall Statistics
Feature vs Bugs
81%Features
Repository Contributions
24Total
Bugs
3
Commits
24
Features
13
Lines of code
1,249
Activity Months2
Your Network
6 peopleSame Organization
@tcu.ac.jp2
Shared Repositories
4
Work History
June 2025
21 Commits • 11 Features
Jun 1, 2025June 2025 (acsl-tcu/common_matlab) — Business-focused technical accomplishments across data workflow, plotting, and validation. Key features delivered include: data preprocessing cleanup and organization to improve modularity and reproducibility; integration of Creating_Datasets.m from an external source to accelerate dataset generation; experiment figure outputs saved to Data/tmp for easier debugging and traceability; gain tuning experiments and conversion gain adjustments enabling faster calibration iterations; enhancements to plotting workflows, including Logger visualization improvements and the development of plot_from_saved_result flows, culminating in final integration of plot_from_saved_result.m and LOGGER.m; creation of figure utilities; plotting from saved results progressed from initial scaffolding to near completion; test execution scaffolding to improve reliability of test runs. Major bugs fixed: supervisor debugging fixes; corrected an incorrect machine/vehicle name; cleanup to remove unused/temporary code. Overall impact: strengthened data pipeline reliability and reproducibility, faster calibration/validation cycles, improved traceability of results, and reduced maintenance burden. Technologies/skills demonstrated: MATLAB scripting, data pipeline engineering, plotting and visualization, debugging, test setup, and version-control discipline.
21 Commits • 11 Features
Jun 1, 2025June 2025 (acsl-tcu/common_matlab) — Business-focused technical accomplishments across data workflow, plotting, and validation. Key features delivered include: data preprocessing cleanup and organization to improve modularity and reproducibility; integration of Creating_Datasets.m from an external source to accelerate dataset generation; experiment figure outputs saved to Data/tmp for easier debugging and traceability; gain tuning experiments and conversion gain adjustments enabling faster calibration iterations; enhancements to plotting workflows, including Logger visualization improvements and the development of plot_from_saved_result flows, culminating in final integration of plot_from_saved_result.m and LOGGER.m; creation of figure utilities; plotting from saved results progressed from initial scaffolding to near completion; test execution scaffolding to improve reliability of test runs. Major bugs fixed: supervisor debugging fixes; corrected an incorrect machine/vehicle name; cleanup to remove unused/temporary code. Overall impact: strengthened data pipeline reliability and reproducibility, faster calibration/validation cycles, improved traceability of results, and reduced maintenance burden. Technologies/skills demonstrated: MATLAB scripting, data pipeline engineering, plotting and visualization, debugging, test setup, and version-control discipline.
June 2025
May 2025
3 Commits • 2 Features
May 1, 2025May 2025 monthly summary for acsl-tcu/common_matlab focusing on delivering longer-horizon simulation capabilities and ML-ready data workflows. The work this month prioritized feature delivery and process improvements that increase test coverage, data quality for ML, and readiness for deployment. No explicit major bugs were reported as fixed in this repository for the month.
May 2025
3 Commits • 2 Features
May 1, 2025May 2025 monthly summary for acsl-tcu/common_matlab focusing on delivering longer-horizon simulation capabilities and ML-ready data workflows. The work this month prioritized feature delivery and process improvements that increase test coverage, data quality for ML, and readiness for deployment. No explicit major bugs were reported as fixed in this repository for the month.
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Quality Metrics
Correctness83.0%
Maintainability83.0%
Architecture75.8%
Performance71.8%
AI Usage20.0%
Skills & Technologies
Programming Languages
MATLAB
Technical Skills
Code DocumentationCode RefactoringControl SystemsData EngineeringData ManagementData PreprocessingData ProcessingData VisualizationDocumentationDrone DevelopmentDrone TechnologyEmbedded SystemsFile HandlingFile ManagementMATLAB Scripting
Repositories Contributed To
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