Course Name: Mathematical Methods and Techniques in Signal Processing

Course abstract

• 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 inclusing convergence issues. • Wavelets: Characterization of wavelets, wavelet transform, multi-resolution analysis. • Statistical signal modeling: The least squares method, Pade’s approximation, Prony’s method, Shanks’ method, iterative pre-filtering, all-pole modeling and linear prediction, autocorrelation and covariance methods, FIR least squares inverse filter design, applications and examples. • Inverse problems (signal reconstruction): regularized methods, reconstruction from projections, iterative methods such as projection onto convex sets


Course Instructor

Media Object

Prof Shayan Srinivasa Garani

Dr. Shayan Garani Srinivasa received his Ph.D. in Electrical and Computer Engineering from Georgia Institute of Technology \u2013 Atlanta, M.S. from the University of Florida \u2013 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.


Teaching Assistant(s)

Praveen J

BSc (research)

Arpit Behera

B.S. (Research)

 Course Duration : Jan-Apr 2020

  View Course

 Enrollment : 18-Nov-2019 to 03-Feb-2020

 Exam registration : 16-Dec-2019 to 20-Mar-2020

 Exam Date : 26-Apr-2020

Enrolled

898

Registered

6

Certificate Eligible

Will be announced

Certified Category Count

Gold

0

Silver

0

Elite

0

Successfully completed

0

Participation

3

Success

Elite

Silver

Gold





Legend

AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75 AND FINAL SCORE >=40
BASED ON THE FINAL SCORE, Certificate criteria will be as below:
>=90 - Elite + Gold
75-89 -Elite + Silver
>=60 - Elite
40-59 - Successfully Completed

Final Score Calculation Logic

  • Assignment Score = Average of best 8 out of 12 assignments.
  • Final Score(Score on Certificate)= 75% of Exam Score + 25% of Assignment Score
Mathematical Methods and Techniques in Signal Processing - Toppers list

Enrollment Statistics

Total Enrollment: 898

Registration Statistics

Total Registration : 18

Assignment Statistics




Assignment

Exam score

Final score

Score Distribution Graph - Legend

Assignment Score: Distribution of average scores garnered by students per assignment.
Exam Score : Distribution of the final exam score of students.
Final Score : Distribution of the combined score of assignments and final exam, based on the score logic.