This repository contains final reports from graduate-level coursework projects in Computer, Mechanical, and Electrical Engineering disciplines. The reports are formatted as research papers, including references to scholarly literature. Projects were completed either individually or in groups.
- Description: This course delves into the fundamentals of deep learning, with a primary focus on the supervised approach. Some exploration of unsupervised methods is also covered, albeit to a lesser extent.
- Topics: ANN, CNN, RNN, Transformers
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Description: In this course, various types of biomedical sensors and their signals are studied, including ECG, EEG, EMG, IMU, among others. The curriculum covers features-based and learning-based techniques, including statistical methods like machine learning.
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- Description: This course covers basic image filtering techniques, including segmentation and clustering, using non-learning-based approaches. It also explores learning-based approaches, including those based on deep learning.
- Description: This course examines single and multi-degree-of-freedom systems, as well as continuous body vibrations, with a specific focus on beams.
- Description: This course focuses on the interaction of MEMS and NEMS devices and their analysis using FEA tools such as Comsol Multiphysics
