Introduction to DSP
- 1. 08/14/18 1 Digital Signal ProcessingDigital Signal Processing Instructor: Engr. Mujeeb Ur RehmanInstructor: Engr. Mujeeb Ur Rehman
- 2. 08/14/18 2 Course Details • Objective – Establish a background in Digital Signal Processing Theory • Required Text – Discrete-Time Signal Processing, – Prentice Hall, 2nd Edition – Alan Oppenheim, Ronald Schafer, John Buck • Grading – Midterm #1: 20% – Midterm #2: 20% – Homework: 20% – Final: 40% • Homework – Problems – MATLAB assignments
- 3. 08/14/18 3 Useful References • Text Books – DSP First A Multimedia Approach • James McClellan, Ronald Schafer, Mark Yoder – Digital Signal Processing, A Computer Science Perspective • Jonathan Stein – A Course in Digital Signal Processing • Boaz Porat • Web Sites – Matlab Tutorial
- 4. 08/14/18 4 DSP is Everywhere • Sound applications – Compression, enhancement, special effects, synthesis, recognition, echo cancellation,… – Cell Phones, MP3 Players, Movies, Dictation, Text-to-speech,… • Communication – Modulation, coding, detection, equalization, echo cancellation,… – Cell Phones, dial-up modem, DSL modem, Satellite Receiver,… • Automotive – ABS, GPS, Active Noise Cancellation, Cruise Control, Parking,… • Medical – Magnetic Resonance, Tomography, Electrocardiogram,… • Military – Radar, Sonar, Space photographs, remote sensing,… • Image and Video Applications – DVD, JPEG, Movie special effects, video conferencing,… • Mechanical – Motor control, process control, oil and mineral prospecting,…
- 5. 08/14/18 5 Course Outline • Introduction to Digital Signal Processing • Sampling of Continuous-Time Signals – Periodic (Uniform) Sampling (4.1) – Frequency-Domain Representation of Sampling (4.2) • Discrete-Time Signals and System – Discrete-Time Signals: Sequences (2.1) – Discrete-Time Systems (2.2) – Linear Time-Invariant Systems (2.3) – Properties of Linear Time-Invariant Systems (2.4) – Linear Constant-Coefficient Difference Equations (2.5) – Freq. Domain Representation of Discrete-Time Signals (2.6) – Representation of Sequences by Fourier Transforms (2.7) – Symmetry Properties of the Fourier Transform (2.8) – Fourier Transform Theorems (2.9) – Reconstruction of a Bandlimited Signal from Its Samples (4.3)
- 6. 08/14/18 6 Course Outline • The Z-Transform – Z-Transform (3.1) – Properties of the Region of Convergence of the z-Transform (3.2) – The Inverse Z-Transform (3.3) – Z-Transform Properties (3.4) • Transform Analysis of Linear Time-Invariant Systems – The Frequency Response of LTI Systems (5.1) – Constant-Coefficient Difference Equations (5.2) – Frequency Response for Rational System Functions (5.3) – Relationship between Magnitude and Phase (5.4) – All-Pass Systems (5.5) – Minimum-Phase Systems (5.6) • Filter Design Techniques – Design of Discrete-Time IIR Filters from Continuous-Time Filters (7.1) – Design of FIR Filters by Windowing (7.2) – Optimum Approximation of FIR Filters (7.4)
- 7. 08/14/18 7 Course Outline • Structures for Discrete-Time Systems – Block Diagram Representation (6.1) – Signal Flow Graph Representation (6.2) – Basic Structures for IIR Systems (6.3) – Transposed Forms (6.4) – Basic Structures for FIR Systems (6.5) – Finite Precision Numerical Effects (6.6) – Effects of Coefficient Quantization (6.7) – Effects of Round-Off Noise in Digital Filters (6.8) • The Discrete-Fourier Transform – Discrete Fourier Series (8.1) – Properties of the Discrete Fourier Series (8.2) – The Fourier Transform of Periodic Signals (8.3) – Sampling the Fourier Transform (8.4) – The Discrete Fourier Transform (8.5) – Properties of the DFT (8.6) • Computation of the Discrete-Fourier Transform
- 8. 08/14/18 8 Signal Processing • Humans are the most advanced signal processors – speech and pattern recognition, speech synthesis,… • We encounter many types of signals in various applications – Electrical signals: voltage, current, magnetic and electric fields,… – Mechanical signals: velocity, force, displacement,… – Acoustic signals: sound, vibration,… – Other signals: pressure, temperature,… • Most real-world signals are analog – They are continuous in time and amplitude – Convert to voltage or currents using sensors and transducers • Analog circuits process these signals using – Resistors, Capacitors, Inductors, Amplifiers,… • Analog signal processing examples – Audio processing in FM radios – Video processing in traditional TV sets
- 9. 08/14/18 9 Limitations of Analog Signal Processing • Accuracy limitations due to – Component tolerances – Undesired nonlinearities • Limited repeatability due to – Tolerances – Changes in environmental conditions • Temperature • Vibration • Sensitivity to electrical noise • Limited dynamic range for voltage and currents • Inflexibility to changes • Difficulty of implementing certain operations – Nonlinear operations – Time-varying operations • Difficulty of storing information
- 10. 08/14/18 10 Digital Signal Processing • Represent signals by a sequence of numbers – Sampling or analog-to-digital conversions • Perform processing on these numbers with a digital processor – Digital signal processing • Reconstruct analog signal from processed numbers – Reconstruction or digital-to-analog conversion A/D DSP D/A analog signal analog signal digital signal digital signal • Analog input – analog output – Digital recording of music • Analog input – digital output – Touch tone phone dialing • Digital input – analog output – Text to speech • Digital input – digital output – Compression of a file on computer
- 11. 08/14/18 11 Pros and Cons of Digital Signal Processing • Pros – Accuracy can be controlled by choosing word length – Repeatable – Sensitivity to electrical noise is minimal – Dynamic range can be controlled using floating point numbers – Flexibility can be achieved with software implementations – Non-linear and time-varying operations are easier to implement – Digital storage is cheap – Digital information can be encrypted for security – Price/performance and reduced time-to-market • Cons – Sampling causes loss of information – A/D and D/A requires mixed-signal hardware – Limited speed of processors – Quantization and round-off errors