Pattern recognition, despite its relatively short history, has already found practical application in many areas of human activity. Systems of pattern recognition usually support people in performing tasks related to ensuring security, including access to premises and devices, detection of unusual changes (e.g. in medicine, cartography, geology), diagnosing technical conditions of devices, and many others. Nevertheless, pattern recognition is probably the most developing area because of the great demand for such solutions in the different areas of our lives. In this book we have collected the experience of scientists from different parts of the world who have researched diverse areas connected directly or indirectly with pattern recognition. We hope that this book will be a treasure trove of knowledge and inspiration for further research in the field of pattern recognition.
This course presents the fundamentals of digital signal processing with particular emphasis on problems in biomedical research and clinical medicine. It covers principles and algorithms for processing both deterministic and random signals. Topics include data acquisition, imaging, filtering, coding, feature extraction, and modeling. The focus of the course is a series of labs that provide practical experience in processing physiological data, with examples from cardiology, speech processing, and medical imaging. The labs are done in MATLAB® during weekly lab sessions that take place in an electronic classroom. Lectures cover signal processing topics relevant to the lab exercises, as well as background on the biological signals processed in the labs.