{"id":9041,"date":"2018-08-25T17:57:45","date_gmt":"2018-08-25T17:57:45","guid":{"rendered":"https:\/\/www.inspirenignite.com\/jntuh\/?p=9041"},"modified":"2026-05-24T10:57:43","modified_gmt":"2026-05-24T05:27:43","slug":"jntuh-m-tech-2017-2018-r17-detailed-syllabus-adaptive-signal-processing","status":"publish","type":"post","link":"https:\/\/www.inspirenignite.com\/jntuh\/jntuh-m-tech-2017-2018-r17-detailed-syllabus-adaptive-signal-processing\/","title":{"rendered":"JNTUH M.Tech 2017-2018 (R17) Detailed Syllabus Adaptive Signal Processing"},"content":{"rendered":"Adaptive Signal Processing Detailed Syllabus for Systems and Signal Processing M.Tech first year first sem is covered here. This gives the details about credits, number of hours and other details along with reference books for the course.\r\n\r\nThe detailed syllabus for Adaptive Signal Processing M.Tech 2017-2018 (R17) first year first sem is as follows.\r\n\r\nM.Tech. I Year I Sem.\r\n\r\nUNIT \u2013I : Introduction to Adaptive Systems Adaptive Systems:<\/strong> Definitions, Characteristics, Applications, Example of an Adaptive System. The Adaptive Linear Combiner – Description, Weight Vectors, Desired Response Performance function – Gradient & Mean Square Error.\r\n\r\nUNIT \u2013II : Development of Adaptive Filter Theory & Searching the Performance surface<\/strong>: Introduction to Filtering – Smoothing and Prediction \u2013 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.\r\n\r\nUNIT \u2013III : Steepest Descent Algorithms:<\/strong> Searching the performance surface \u2013 Methods & Ideas of Gradient Search methods – Gradient Searching Algorithm & its Solution – Stability & Rate of convergence – Learning Curves Gradient Search by Newton\u2019s Method, Method of Steepest Descent, Comparison of Learning Curves.\r\n\r\nUNIT \u2013IV : LMS Algorithm & Applications<\/strong>: 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\r\n\r\nUNIT \u2013V : State Estimators:<\/strong> 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.\r\n\r\nTEXT BOOKS:<\/strong>\r\n