Post graduates and senior UGs with a strong background in basic DSP.
PREREQUISITES:UG in Digital Signal Processing, familiarity with probability and linear algebra
INDUSTRY SUPPORT:Any company using DSP techniques in their work, such as, TI, Analog Devices, Broadcom and many more.
Dr. Shayan Garani Srinivasa received his Ph.D. in Electrical and Computer Engineering from Georgia Institute of Technology – Atlanta, M.S. from the University of Florida – Gainesville and B.E. from Mysore University. Dr. Srinivasa has held senior engineering positions within Broadcom Corporation, ST Microelectronics and Western Digital. Prior to joining IISc, Dr. Srinivasa was leading various research activities, managing and directing research and external university research programs within Western Digital. He was the chairman for signal processing for the IDEMA-ASTC and a co-chair for the overall technological committee. He is the author of a book, several journal and conference publications, holds U.S patents in the area of data storage. Dr. Srinivasa is a senior member of the IEEE, OSA and the chairman for the Photonic Detection group within the Optical Society of America. His research interests include broad areas of applied mathematics, physical modeling, coding, signal processing and VLSI systems architecture for novel magnetic/optical recording channels, quantum information processing, neural nets and math modeling of complex systems.
Week 1 : Review of vector spaces, inner product spaces, orthogonal projections, state variable representation
Week 2 : Review of probability and random processes
Week 3 : Signal geometry and applications
Week 4 : Sampling theorems multirate signal processing decimation and expansion (time and frequency domain effects)
Week 5 : Sampling rate conversion and efficient architectures, design of high decimation and interpolation filters, Multistage designs.
Week 6 : Introduction to 2 channel QMF filter bank, M-channel filter banks, overcoming aliasing, amplitude and phase distortions.
Week 7 : Subband coding and Filter Designs: Applications to Signal Compression
Week 8 : Introduction to multiresolution analysis and wavelets, wavelet properties
Week 9 : Wavelet decomposition and reconstruction, applications to denoising
Week 10 : Derivation of the KL Transform, properties and applications.
Week 11 : Topics on matrix calculus and constrained optimization relevant to KL Transform derivations.
Week 12 : Fourier expansion, properties, various notions of convergence and applications.
SUGGESTED READING MATERIALS: