Mathematical Methods and Techniques in Signal Processing
- Review of basic signals, systems and signal space: Review of 1-D signals and systems, review of random signals, multi-dimensional signals, review of vector spaces, inner product spaces, orthogonal projections and related concepts.
- Sampling theorems (a peek into Shannon and compressive sampling), Basics of multi-rate signal processing: sampling, decimation and interpolation, sampling rate conversion (integer and rational sampling rates), oversampled processing (A/D and D/A conversion), and introduction to filter banks.
- Signal representation: Transform theory and methods (FT and variations, KLT), other transform methods including convergence issues.
- Wavelets: Characterization of wavelets, wavelet transform, multi-resolution analysis.
INTENDED AUDIENCE : Post graduates and senior UGs with a strong background in basic DSP.PRE-REQUISITES : UG in Digital Signal Processing, familiarity with probability and linear algebraINDUSTRY SUPPORT : Any company using DSP techniques in their work, such as, TI, Analog Devices, Broadcom and many more.Rajeev Motwani and Prabhakar Raghavan,Randomized Algorithms
- Fecha Incio:18/01/2021
- Idioma: Inglés
- Universidad: Indian Institute of Science Bangalore
- Profesores: Shayan Garani Srinivasa
- Certificado: No