JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD

UNIT - I

Introduction to Adaptive Systems Adaptive Systems: Definitions, Characteristics, Applications, Example of an Adaptive System. The Adaptive Linear Combiner - Description, Weight Vectors, Desired Response Performance function - Gradient & Mean Square Error.

UNIT - II

Development of Adaptive Filter Theory & Searching the Performance surface: Introduction to Filtering - Smoothing and Prediction – Linear Optimum Filtering, Problem statement, Principle of Orthogonally - Minimum Mean Square Error, Wiener- Hopf equations, Error Performance - Minimum Mean Square Error, Estimation of phase shift between two narrow band signals using Orthogonal Decomposer.

UNIT - III

Steepest Descent Algorithms: Searching the performance surface – Methods & Ideas of Gradient Search methods - Gradient Searching Algorithm & its Solution - Stability & Rate of convergence - Learning Curves Gradient Search by Newton’s Method, Method of Steepest Descent, Comparison of Learning Curves.

UNIT - IV

LMS Algorithm & Applications: Overview - LMS Adaptation algorithms, Stability & Performance analysis of LMS Algorithms - LMS Gradient & Stochastic algorithms - Convergence of LMS algorithm. Applications: Adaptive BFSK, BPSK, ASK demodulators and delay estimation. Adaptive Beam forming, concept of IQ channels, Adaptive filter implementation of Hilbert Transform. Introduction to MUSIC

UNIT - V

State Estimators: Introduction to RLS Algorithm, Statement of Kalman filtering problem, The Innovation Process, Estimation of State using the Innovation Process- Expression of Kalman Gain, Filtering Example estimation of state from observations of noisy observed narrow band signals. Target tracking using only DOA.

TEXT BOOKS:

• 1. Adaptive Signal Processing - Bernard Widrow, Samuel D. Strearns, 2005, PE.
• 2. Adaptive Filter Theory - Simon Haykin-, 4th Edition, 2002, PE Asia.

REFERENCE BOOKS:

• 1. Kaluri V. Rangarao, Ranjan K. Mallik, “Digital Signal Processing: A Practitioner's Approach”, ISBN: 978- 0-470-01769-2, 210 pages, November 2006, John Wiley (UK) nd
• 2. Sophocles. J. Orfamadis, “Optimum signal processing: An introduction”, 2 Edition, 1988, McGraw-Hill, New York
• 3. S. Thomas Alexander, “Adaptive signal processing-Theory, and Applications, 1986, Springer –Verlag.
• 4. Candy, “Signal analysis”, McGraw Hill Int. Student Edition
• 5. James V. Candy, “Signal Processing: A Modern Approach”, McGraw-Hill, International Edition, 1988. M. TECH. I Year II Semester Biomedical Signal Processing and Instrumentation ADVANCED MEDICAL SIGNAL & IMAGE PROCESSING LABORATORY

Note:

• Minimum of 10 Experiments to be conducted.
• All Simulations are to be carried out using MATLAB/DSP PROCESSORS/LAB VIEW SOFTWARE (Or any equivalent software) & DSP KITS. Medical Signal Processing:
• 1. Least Squares, Orthogonality, and Fourier series
• 2. Correlation, Fourier Spectra and the Sampling Theorem
• 3. Linear systems and Transfer Function
• 4. FIR Filter Design for Biomedical signal processing
• 5. IIR Filter Design for Biomedical signal processing
• 6. ECG noise cancellation
• 7. Biomedical Signal Compression
• 8. Detection of QRS complex and heart rate measurement. Medical Image Processing:
• 1. Study of Basic commands in MATLAB
• 2. Image Linear Filtering and Transforms
• 3. Image Segmentation
• 4. Image Restoration techniques
• 5. Image registration
• 6. Image Analysis
• 7. Enhancement and restoration
• 8. Morphological Operation