# ADVANCED TECHNIQUES IN SIGNAL PROCESSING

 Course Code : P1ECBC05 Author : uLektz University : Biju Patnaik University of Technology (BPUT) Regulation : 2016 Categories : Electronics & Communication Format : ePUB3 (DRM Protected) Type : eBook

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Description :ADVANCED TECHNIQUES IN SIGNAL PROCESSING of P1ECBC05 covers the latest syllabus prescribed by Biju Patnaik University of Technology (BPUT) for regulation 2016. Author: uLektz, Published by uLektz Learning Solutions Private Limited.

Note : No printed book. Only ebook. Access eBook using uLektz apps for Android, iOS and Windows Desktop PC.

##### Topics
###### UNIT - I INTRODUCTION TO DSP SYSTEM, ITERATION BOUND, PIPELINING AND PARALLEL PROCESSING, RETIMING, UNFOLDING AND FOLDING

1.1 Introduction to DSP System: Representation of DSP algorithms

1.2 Iteration Bound: Data-flow graph representations, Loop bound and iteration bound

1.3 Algorithms for computing iteration bound, Iteration bound of multirate data-flow graphs

1.4 Pipelining and Parallel Processing: Pipelining of FIR digital filters, Parallel processing, Pipelining and parallel processing for low power

1.5 Retiming: Definitions and properties, Solving systems of inequalities, Retiming techniques

1.6 Unfolding: An algorithm for unfolding, Properties of unfolding, Critical path, unfolding and retiming, Applications of unfolding

1.7 Folding: Folding transformation, Register minimization techniques, Register minimization in folding architectures, Folding of multirate systems

###### UNIT - II WIENER FILTERING AND SPECTRUM ESTIMATION

2.1 Wiener Filtering: Introduction, The FIR Wiener Filter-Filtering, Linear Prediction, Noise Cancellation

2.2 IIR Wiener Filter-Non-causal IIR Wiener Filter, The Causal IIR Wiener Filter, Causal Wiener Filtering, Causal Linear Prediction, Wiener Deconvolution, Discrete Kalman Filter

2.3 Spectrum Estimation: Introduction, Non-parametric Method-The Periodogram, Performance of Periodogram

2.4 Parametric Methods-AR Spectrum Estimation, MA Spectrum Estimation, ARMA Spectrum Estimation

2.5 Frequency Estimation- Eigen decomposition of the Autocorrelation Matrix, MUSIC

###### UNIT - III ADAPTIVE FILTERING

3.2 The LMS Algorithm, Convergence of LMS Algorithm

3.3 NLMS, Noise Cancellation, LMS Based Adaptive Filter, Channel Equalization

3.4 Adaptive Recursive Filter, RLS- Exponentially Weighted RLS, Sliding Window RLS

###### UNIT - IV CARDIOVASCULAR SYSTEM, ANALOG SIGNAL PROCESSING OF BIOSIGNALS, TIME-FREQUENCY REPRESENTATIONS, BIOMEDICAL APPLICATIONS AND NOISE

4.1 Cardiovascular system: Heart structure, Cardiac cycle, ECG (Electrocardiogram) theory (B.D.), PCG (Phonocardiogram), EEG

4.2 X-Ray

4.3 Sonography, CT-Scan

4.4 The nature of biomedical signals, Analog signal processing of Biosignals: Amplifiers, Transient Protection, Interference Reduction, Movement Artifact Circuits

4.5 Active filters, Rate Measurement, Averaging and Integrator Circuits, Transient Protection circuits

4.6 Time-frequency representations: Introduction, Short-time Fourier transform, Spectrogram, Wavelet signal decomposition

4.7 Biomedical applications: Fourier, Laplace, Z-transforms

4.8 Autocorrelation, Cross-correlation, Power spectral density

4.9 Noise: Different sources of noise, Noise removal and signal compensation