M.Tech, Syllabus

JNTUH M.Tech 2017-2018 (R17) Detailed Syllabus Signal Processing Lab – II

Signal Processing Lab – II Detailed Syllabus for Systems and Signal Processing M.Tech first year second sem is covered here. This gives the details about credits, number of hours and other details along with reference books for the course.

The detailed syllabus for Signal Processing Lab – II M.Tech 2017-2018 (R17) first year second sem is as follows.

M.Tech. I Year II Sem.

Note:

  • Minimum of 10 Experiments have to be conducted
  • All Simulations are be carried out using MATLAB/DSP Processors/Labview Software & DSP Kits(or any other equivalent software)
  1. Study of various addressing modes of DSP using simple programming examples
  2. Generation of waveforms using recursive/filter methods
  3. Sampling of input signal and display
  4. Implementation of Linear and Circular Convolution for sinusoidal signals
  5. Framing & windowing of speech signal.
  6. Finding voiced & unvoiced detection for each frame of speech signal.
  7. IIR Filter implementation using probe points
  8. Implementation of FIR filters on DSP processor
  9. Loop back using DSK kit
  10. Real time signal enhancement using Adaptive Filter.
  11. Representation of different Q-formats using GEL function
  12. Verification of Finite word length effects (Overflow, Coefficient Quantization, Scaling and Saturation mode in DSP processors)
  13. Image enhancement using spatial & frequency domain
  14. Implementation of Image segmentation techniques
  15. Extraction of frames from Video signal

For all other M.Tech 1st Year 2nd Sem syllabus go to JNTUH M.Tech Systems and Signal Processing 1st Year 2nd Sem Course Structure for (R17) Batch.

All details and yearly new syllabus will be updated here time to time. Subscribe, like us on facebook and follow us on google plus for all updates.

Do share with friends and in case of questions please feel free drop a comment.

Leave a Reply

Your email address will not be published. Required fields are marked *

*

This site uses Akismet to reduce spam. Learn how your comment data is processed.