Acquiring and writing data using Python

3. To know about the finite word length effects and PDSPs

Course Outcomes

At the end of this course, students will demonstrate the ability to 1. Explain the digital signal processing concepts. 2. Analyze the discrete time signals for DSP applications. 3. Apply various transformations for Digital (IIR and FIR) filter design procedures 4. Relate the signal processing concepts practically with the help of finite word length effects and PDSPs. 5. Compare and select the DSP processor suitable for a specific application. 6. Design and develop algorithms for signal processing applications

Module 1: Introduction to Signals and Systems (7 Hours)

Classification of signals: continuous and discrete, energy and power, Mathematical representation of signal, Classification of systems: Continuous, discrete, linear, causal, stability, dynamic, time variance.

Module 2: Introduction to DSP and Fourier Transform (7 Hours)

Review of Discrete Time LTI Systems – Linear, circular and sectioned convolutions. Sample rate conversion. Discrete fourier transform. Fast Fourier transforms computations using DIT and DIF algorithms.

Module 3: Infinite Impulse Response Filters (8 Hours)

Calculation of IIR coefficients using pole –zero placement method- Review of classical analog filtersButterworth, Chebyshev and Elliptic filters–Transformation of analog filters into equivalent digital filters using impulse invariant method and Bilinear transformation method.

Module 4: Finite Impulse Response Filters (8 Hours)

Symmetric and Antisymmetric FIR filters –Linear phase response and its implication –FIR filter design using window method – frequency sampling method – design of optimal linear phase FIR filters –realization structures of FIR filters – transversal and linear phase structures.

Module 5: Finite Word Length Effect (8 Hours)

Representation of numbers in registers-ADC quantization noise-coefficient quantization error-Product quantization error –Limit cycles due to product round-off error, Round –off Noise reduction schemeAddition over flow errors-Principle of scaling.

Module 6: Adaptive filtering and DSP Processors (7 Hours)

Adaptive filtering – basic wiener filter theory – LMS adaptive algorithm. Introduction to general and special purpose hard ware for DSP – Harvard architecture –pipelining-Special Instruction-ReplicationHardware digital filter – Texas Instruments TMS320C6416 – Instruction set of TMS320C6416 –Simple programs. Applications of DSP – Case studies.

Text Books

1. JohnGProakisandManolakis,“DigitalSignalProcessingPrinciples,Algorithmsand Applications”, Pearson, 4th Edition,2007. 2. Alan V. Oppenheim and Ronald W. Schafer, “Discrete-Time Signal Processing”, Prentice Hall, New Jersey, 3rd Edition, 2010.

Reference Books

1. Emmanuel C. If eacher and Barrie W. Jervis, “Digital Signal Processing –A Practical Approach”, Wesley Longman Ltd., 2nd Edition,2004 2. SanjitK. Mitra, “Digital Signal Processing - A Computer Based Approach”, Tata McGraw-Hill, New Delhi, 2nd Edition, 2001. 3. Johny R. Johnson, “Introduction to Digital Signal Processing”, PHI,2006 4. S. Salivahanan, A. Vallavaraj, C. Gnanapriya, “Digital Signal Processing”, McGraw Hill International,2007 5. Venkatramani B, M. Bhaskar, ‘Digital Signal Processors Architecture, Programming and Applications’, Tata McGraw– Hill Publishing Company Limited, New Delhi, 2002.