The course aims at providing the students with the exaustive knowledges concerning the methods and the technologies useful to develop embedded systems. Applying the signal processing methods, the students will experience the three embedded systems information layers: the analog signal layer (physical), the mixed-signal layer (analog/digital) and the digital signal layer (DSP). Softcomputing methods will be proposed to the student as alternative methods to hardcomputing methods (algoritm-based) to process the fuzzy and non linear information of the real physic world. The student will be provided of the state-of art of the softcomputing paradigms (fuzzy logic and artificial neural networks) to be deployed on hardware computing platforms for simulation and fast prototyping of (commercial of-the shelf) hardware-based real-time embedded systems design. The student will be provided of in-deep knoledge of the key issues concerning the deploying of the digital algorithms on computational machines, requiring application specific computing architectures such as Digital Signal Processors (DSPs).
Expected learning outcomes
At the end of the course, the student will be provided of an exaustive knoledge of the embedded nature of the signal information related to its measurement domain (spatio-temporal, frequency) and of the digital processing issues of such information targetted to the be deployed on general purpose processors (MCUs) and application specific processors such as Digital Signal Processors (DSPs). The acquired methods of softcomputing will enable the student to approach the development of applications that cannot be approached with the hardcomputing (algoritm-based) methods. Such methods will enable the student also to chose the optimal computing architecture to develope real-time performing embedded systems.
Lesson period: First semester
(In case of multiple editions, please check the period, as it may vary)