Signal Analysis is the extraction of meaningful information from signals. This is often digital images but it can also be signals from other sensor modalities (sound, lasers, radars, accelerometers, etc.). The purpose can be to identify an object or a person, understand what a person is doing, evaluate how she/he is doing it, or localize an object or a person in an environment.
Mechatronics is the joint disciplinary subject in the intersection between machine engineering, electronics, computer and systems engineering. Mechatronics includes control theory, computer science, signal processing and sensor technology. Today’s vehicles and robots are excellent examples of modern complex mechatronic systems.
Machine learning is about designing algorithms that can make machines (computers) develop knowledge (learn) from empirical observation from, e.g., sensors and data bases, and from interaction with humans. Key parts in machine learning are supervised and unsupervised analysis and recognition of patterns and how “intelligent” decisions can be made from this.
Contact:
Antanas Verikas, Professor Pattern Recognition and Machine Learning
