Proposed master’s theses

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My catalogue of proposed master’s thesis topics:

• Non-linear neuro-controller for automatic control tasks
• Use of Raspberry Pi for the adaptive identification and management of hydraulic systems
• Deep learning for visual recognition
• Planning of energy-efficient routes for industrial robots
• Self-tuning of PID controllers for robot manipulators
• Monitoring of production-technology processes
• Characterization and analysis of micro-CT of biomedical tissues
• Predicting the behaviour of a soft robot using machine learning
• Global optimization study using particle swarms
• Rigid-body movement as a predictor of wave state
• Calibration of a heliostat with drones
• Quantifying the impact of sensor capabilities on UAV localization
• Restriction of the tracking algorithm for SLAM features with body mode
• High-precision dimensional metrology
• Development of a microfluidic device for isolating bacteria from bodily fluids
• Machine learning on co-registered CT-PET scans to identify TB lesions in the lungs
• Adaptive control of an automatic pneumatic pressure regulator
• Classification of cardiac arrhythmias using neural networks
• Neural networks for the reconstruction of thermal images
• Higher-order unit for modelling the fuel consumption of an aircraft
• Validation of aerodynamic profile data
• Architecture for the early detection of hyperglycaemia
• Low-cost hardware for ergonomic applications
• Wireless physiological measurement system using FPGA and Bluetooth
• Collaborative human–machine interaction in industrial environments
• Characterization and analysis of low-cost biomedical platforms
• Multi-sensor mHealth system based on Android
• Smartphone-based monitoring system for long-term sleep evaluation
• Contextualized application for controlling mobile robots using physiological data
• A web platform for viewing and obtaining biosignals in real time
• Adaptive control of a robotic prosthesis
• Linear discriminant analysis for electrocardiogram signal processing
• Non-stationary analysis of biomedical signals
• A predictive model using the Hilbert–Huang transform and fuzzy logic
• Comparative analysis of noise filtering in biomedical signals using wavelets and the Hilbert–Huang transform
• Feature extraction in electrocardiograms applying independent component analysis (ICA) and the Fourier transform
• Non-stationary analysis of neonatal electrophysiological signals
• Feature extraction in spinogram signals and analysis of their non-stationarity
• SARIMA model for the prediction of climate change
• Time-series machine learning using embedded time delays and precision annealing
• Deep learning for computer vision with Python
• Neural machine translation technology
• Applied control
• Signal processing for ECG

The area of signal processing, control and artificial neural networks belongs to the “Informatics” research group and has several active projects. Working successfully in this area requires a solid programming background, the ability to read and write well in English and Spanish, a good mathematical foundation and a positive attitude towards both teamwork and independent work. See the Publications and Research projects sections for more detailed information about current projects.

As a thesis student, your research activities will involve the following operating systems, software and programming languages:

• Linux
• Python
• C/C++
• LaTeX

A good way to find out whether a research-oriented master’s degree is for you is to do your internship or professional residence in our group. For more information, see my personal page www.rodriguezricardo.net. For calendars, admission procedures and other aspects of postgraduate studies at Tecnun, see the page of the Informatics Department.

If you would like to know more about the projects or obtain a postgraduate degree in this area, please email Dr. Ricardo Rodríguez Jorge or visit our facilities in Donostia, Spain. Thank you for your interest.