Master's student in Computer Engineering
Specialization: Medical Technology
I am a Master's student in Computer Engineering with a specialization in medical technology and health technology.
My background covers a broad range of computer engineering: algorithms, data structures, operating systems, and embedded systems. Beyond this, I have applied knowledge particularly in biosignal processing, medical technology systems, and data analysis. I have worked with real biosignals such as ECG, PPG, EOG, and EMG.
I am accustomed to working in teams, both in academic projects and in practical, technical environments. Documentation and systematic methodology are a core part of my working approach, especially when collaborating in multidisciplinary groups.
Beyond the technical, I have a strong interest in psychology, particularly in combination with IT systems.
I regularly collaborate in multidisciplinary teams. Through organizational work I have become accustomed to leading and coordinating teams. As an employee at Viking Line I communicated professionally with customers and staff on a daily basis.
Practical troubleshooting of technical systems. I am accustomed to working methodically according to documentation and instructions.
Systematic approach to both technical work and project management.
I am an experienced leader both theoretically and in practice. I am accustomed to taking responsibility in groups and coordinating between different stakeholders. I have received formal leadership training through a university course, my military service, and the scouting organization.
Non-commissioned officer.
Scout leader since 2020.
Board work in student organization.
Board game timer in the style of a chess clock but for multiple players. Built with vanilla JavaScript.
Embedded systems engineer in a 6-month university project. I am responsible for developing an embedded device that reads EV data via the OBD-2 diagnostics port and forwards lightly processed data to a server.
Skills:
Collaboration
Planning
Documentation
Problem solving
Implementation
Software production
Project presentation & pitching
Hardware development
Market research
The project aimed to automatically classify sleep stages (wake, NREM, REM) from EOG and EMG signals using machine learning. Raw data was preprocessed with Butterworth bandpass filters (EOG: 0.5–50 Hz, EMG: 20–99 Hz) and features were extracted in the frequency domain via Welch's method, including dominant frequency, spectral entropy, centroid, and rolloff.
Two classification models were compared: Random Forest and SVM with RBF kernel. Hyperparameters were optimized with GridSearchCV and RFECV was used to select an optimal feature subset. Random Forest achieved ~82% accuracy and proved more robust across all classes, while SVM achieved ~77%.
Technologies: Python, NumPy, SciPy, scikit-learn
The thesis examines security vulnerabilities in GNSS systems, primarily spoofing and jamming, and evaluates alternative and complementary positioning methods for situations where the GNSS signal is unreliable or unavailable. The work combines a systematic literature review with technical analysis of the methods' performance, reliability, and practical applicability.
Grade: 5/5
Note: This thesis is written in Swedish.
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