Sensing and data analysis is fundamental to all Engineering disciplines. It relies on a key understanding of sensor networks and how they communicate, resource and computation constraints, and an understanding of how data is sampled and then analysed. Signals are the output of sensors which have measured data, and this course gives an introduction to key signal analysis concepts.

This course aims to introduce students to the fundamentals of Sensor Networks, Signal Processing, Communication, and Information Theory. The course aims to provide an insight into time domain and frequency domain analysis of continuous-time signals, and provide an insight into the sampling process and properties of the resulting discrete-time signals. The course then introduces the students to basic communication modulation techniques, as well as probability theory for analysing random signals. At the end of the module students will have acquired sufficient expertise in these concepts to appreciate how sensor networks and signal analysis can be used in a variety of disciplines.