Signal and transmission theory. Metric space, linear space, Hilbert’s space. Dirac delta function and its properties. Periodic function and Fourier series. Non periodic function, Fourier’s integral. Convolution. Impulse signal. Sampling theore. Discrete Fourier transform and FFT. Analogic and digital filters. Definition of information and its measure. Entropy. Axiomatic formulation of the Information Theory. Elements of encoding theory. JPEG compression.
Lecture Notes.
D. J. C. MacKay "Information Theory, Inference, and Learning Algorithms". Cambridge University Press 2003.
Learning Objectives
Educational Objectives:
Basic concepts on Information theory.
Acquired Competences:
Knowledge of basic concepts of signal theory, sampling problem of data and their reconstruction.
Acquired Skills. (at the end of the course):
Handling of Fourier transform and Dirac delta function. Use of the sampling theorem and its applications.
Prerequisites
None
Teaching Methods
CFU: 6
Total hours of the course (including the time spent in attending lectures, seminars, private study, examinations, etc...): 150
Hours reserved to private study and other indivual formative activities: 102
Contact hours for: Lectures (hours): 48
Further information
.
Type of Assessment
Oral exam
Course program
Signal and transmission theory. Metric space, linear space, Hilbert’s space. Dirac delta function and its properties. Periodic function and Fourier series. Non periodic function, Fourier’s integral. Convolution. Impulse signal. Sampling theore. Discrete Fourier transform and FFT. Analogic and digital filters. Definition of information and its measure. Entropy. Axiomatic formulation of the Information Theory. Elements of encoding theory. JPEG compression.