Signal Processing

Digital signal processing is used in many modern computer sciences systems and applications. Examples are machine learning, artificial intelligence, and big data analysis; content recommenders in information systems; speech, music and image content based retrieval and searching; music and video compression; sensor data processing in embedded systems; bioinformatics and medical data analysis. This course deals with the foundations and principles of digital signal processing. The first part concentrates on acquiring digital signals (sampling) and the basic linear filtering operations and convolution. The second part of the course introduces the concept of frequency or Fourier description of signals and systems. This concept is the foundation of many of today’s computer science-based systems and applications, and has found wide applications in the processing of a variety of sound, music, sensor, image, video and other multimedia information.