Lecture_1 (1).pptx
- 2. Pre-Requisites • Signals and Systems • Signal Basics/Transformations • System Basics • Transforms (Fourier, Laplace) • MATLAB ® • Think and Test Approach • No need to reinvent the wheel • Albeit with its limitations
- 3. Course Distribution – 3 Credit Hour 3 •Quizzes (5-6) 10% •Assignments (3-4) 5% •Midterm 30% •Complex Engineering Problem 15% •Final 40% © Hira Ali Jamal
- 4. Relevant Books • Textbook: (Prentice-Hall signal processing series) Oppenheim, Alan V._ Schafer, Ronald W - Discrete-Time Signal Processing-Pearson (2009) • Reference Book:
- 5. Signals & Systems ? • Signal Definition? • representation of information • a physical quantity that is a function of any independent variable (time, space, wavelength, …) • measured quantity that varies with time (or position) • Examples? • electrical signal received from a transducer (microphone, thermometer, accelerometer, antenna, etc.) • amount of precipitation over time; speech signal interpreted by your brain • System Definition? • a physical entity that processes the signal in some manner • Examples ? human ear; a board marker,… • defined by models
- 6. What is Signal Processing (Kuhn 2005) Signals may have to be transformed in order to amplify or filter out embedded information detect patterns prepare the signal to survive a transmission channel undo distortions contributed by a transmission channel compensate for sensor deficiencies find information encoded in a different domain. To do so, we also need: methods to measure, characterize, model, and simulate signals. mathematical tools that split common channels and transformations into easily manipulated building blocks.
- 7. Signal Processing Signal Processing System Input signal Output signal • Function : extract (output) desired information (e.g. filtering, parameter estimation) analog system signal output continuous-time signal continuous-time signal discrete- time system signal output discrete-time signal discrete-time signal digital system signal output digital signal digital signal
- 8. Signal Processing A/D DSP D/A analog signal analog signal digital signal digital signal • Analog input – analog output : Example – Digital recording of music • Analog input – digital output: Example – Touch tone phone dialing • Digital input – analog output : Example – Text to speech • Digital input – digital output : Example – Compression of a file on computer Signal Processing System Input signal Output signal • Function : extract (output) desired information (e.g. filtering, parameter estimation) Processing Examples
- 22. Analog Electronics for Signal Processing (Kuhn 2005) Passive networks (resistors, capacities, inductivities, crystals, nonlinear elements: diodes …), (roughly) linear operational amplifiers Advantages: passive networks are highly linear over a very large dynamic range and bandwidths. analog signal-processing circuits require little or no power. analog circuits cause little additional interference
- 23. Digital Signal Processing (Kuhn 2005) Analog/digital and digital/analog converters, CPU, DSP, ASIC, FPGA Advantages: noise is easy to control after initial quantization highly linear (with limited dynamic range) complex algorithms fit into a single chip flexibility, parameters can be varied in software digital processing in insensitive to component tolerances, aging, environmental conditions, electromagnetic inference Relatively small size Digital circuits can easily be mass produced, for example in VLSI form. Desired accuracy can be achieved by increasing wordlength, within any cost limitation Digital processors can be time-shared Adaptive processing is permitted Storage of digital signals is straightforward
- 24. Why Digital Signal Processing • Disadvantage include: • Need for A/D and D/A processing • can require significantly more power (battery, cooling) • digital clock and switching cause interference
- 25. DSP-Where? • Communication – Signal Processing for Communication • Channel Estimation, Echo cancellation, Detection, etc. • Cellular phones, Satellite receivers, Modems, etc. Courtesy : Lake of Soft
- 26. DSP-Where? Contd. • Speech (sound) applications • Speech compression, speaker identification, speech enhancement in noisy environment, special effects • Cell Phones, MP3 Players, Movies, Dictation, Text-to-speech,… Speech enhancement Speaker identification Courtesy : DSP Development Corp. Courtesy : Busim SPG Courtesy : HumanScan GmBH
- 27. DSP-Where? Contd. • Image Processing • Image is after all a 2-D signal Raw Image Filtered Image Object tracking Courtesy : ISIR, OSAKA Univ.
- 28. DSP-Where? Contd. • Biomedical Signal Processing • Magnetic Resonance, Tomography, Electrocardiogram,… Courtesy : Srinivasan
- 29. DSP-Where? Contd. • Military • Radar, Sonar, Space photographs, remote sensing Courtesy : IAI Courtesy : CRISP, NUS
- 30. DSP-Where? Contd. • Mechanical • Motor control, process control,… • Automotive • ABS, GPS, Active Noise Cancellation, Cruise Control, Parking,…
- 31. Course Outline -Tentative 31 © Hira Ali Jamal
- 32. 32 Week Description 1 Introduction to discrete-time signals and systems. Mathematical description of discrete-time signals and systems both in time domain as well as in frequency domain, sequences, series, convergence. 2 The Discrete-time Fourier Transform Discrete Fourier series and its properties, Discrete Fourier transform and its properties 3 Computation of the Discrete-time Fourier Transform Efficient computation of the DFT using decimation-in-time and decimation-in- frequency algorithms. 4 The Z transform – 1 Forward and Inverse Z transform, Properties of the region of convergence (ROC) 5 The Z transform – 2 Properties of Z transform 6 Sampling of Continuous-Time Signals Frequency domain representation of sampling, Reconstruction of a bandlimited signal from its samples 7 Multirate Signal Processing - 1 Changing the sampling rate of discrete-time signals. 8 Multirate Signal Processing – 2 © Hira Ali Jamal
- 33. 33 Week Description 9 Transform Analysis of Discrete-Time Systems – 1 Minimum phase systems, Generalized linear phase systems. 10 Transform Analysis of Discrete-Time Systems – 2 Minimum phase systems, Generalized linear phase systems. 11 Filter design techniques - 1 Laplace Transform, Design of analog filters 12 Filter design techniques – 2 Design of discrete-time IIR filters from continuous-time filters. 13 Filter design techniques – 3 Design of discrete-time FIR filters. 14 Filter design techniques – 3 Design of discrete-time FIR filters. 15 Computation of the Discrete-time Fourier Transform Efficient computation of the DFT using decimation-in-time and decimation-in- frequency algorithms. 16 Revision © Hira Ali Jamal