ECE3123_Chapter-01_Introduction - ECE3221 Digital Signal Processing Prof Dr Othman O Khalifa Electrical and Computer Engineering Kulliyyah of

ECE3123_Chapter-01_Introduction - ECE3221 Digital Signal...

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Unformatted text preview: ECE3221 Digital Signal Processing Prof. Dr. Othman O. Khalifa Electrical and Computer Engineering Kulliyyah of Engineering International Islamic University Malaysia Programme Objectives CE Engineering) 1. 2. 3. 4. 5. Event Name To produce graduates with broad based knowledge and fundamentals of engineering to solve problems, generate new ideas and develop products for the need of the society. To produce graduates with competencies in engineering design and analysis. To produce successful and productive engineers with skills in communication, management, teamwork and leadership. To produce graduates with good understanding of moral values, professional ethics and responsibility toward society and environment. To produce graduates who recognize the importance of and engage in life-long learning. Programme Learning Outcome 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. The ability to acquire and apply knowledge of Mathematics, science and engineering science fundamentals. The ability to acquired a broad based education necessary to understand the impact of understand engineering solutions in a global and societal context. The ability to have in depth understanding and technical competency in Computer and competency information/ Communication Engineering. The ability to undertake problem identification, formulation and solution. The ability to design a system, component or process for operational performance. operational The ability to design and conduct experiments as well as to analyze and interpret data. analyze The ability to understand the principles of sustainable design and development. and The ability to effectively communicate orally, in writing and using multimedia tools. using The ability to function effectively as an individual and in group with the capacity to be a leader group or manager as well as an effective team leader member. The ability to recognize the need for life long learning and posses the ability to pursue posses independent learning for professional development. The ability to understand the social, cultural, global and environmental responsibilities of a environmental professional engineer and the need foe sustainable development. The ability to understand and commit to professional and ethical responsibilities. The ability to understand the expectations of an engineer who practices in an industrial or practices governmental organization. Introduction The aims of this Course are: To introduce the student to the digital signal analysis To provide a background for understanding of digital filters design. To expose the student to the implementation of Discrete-Time Systems. DiscreteTo introduce Z-transfrom, Fourier Transform Z- transfrom, To explain the sampling & Quantization of continuous Time signals Apply DSP in many areas of Engineering. To explain the typical structure and the benefits of DSP systems To highlight some applications of DSP Event Name Course Assessment Quizzes 15% Laboratory assignment Mid-term Examination Final Examination 10% 25% 50% Textbooks Digital Signal Processing:Principles, Algorithms and Applications, John G. Proakis and D. G. Manolakis, 2007, Prentice Hall. Discrete-Time Signal Processing, Oppenheim, Schafer, DiscreteProcessing, Oppenheim, and Buck. Signals and Systems, Oppenheim, Willsky, and Young. Systems, Oppenheim, Willsky, Theory and Applications of Digital Signal Processing, Processing, Rabiner and Gold. Digital Filters: Analysis and Design, Antoniou. Design, Digital Signal Processing :efficient convolution and fourier Transform techniques, Myers D. G, 1990, Prentice Hall Digital Signal Processing, Cavicchi T. J., 2000, Wiley & Sons Event Name Digital Signal Processing (DSP) Introduction • DSP is the processing of signals by digital means, it is the study of systems by changing or analysing information which is measured as discrete sequence of numbers. Digital Signal Processing (DSP) is a branch of signal processing that emerged from the rapid development of VLSI technology that made feasible real-time digital realcomputation. • DSP involves time and amplitude quantization of signals and relies on the theory of discrete-time discretesignals and systems. • DSP emerged as a field in the 1960s • Early applications of off-line DSP include seismic data offanalysis and vocoder research. Introduction to DSP There are two types of DSP applications, namely; non-real-time and non- realreal time. Non-real-time signal processing involves manipulating Non- realsignals that have already been collected and digitized while realreal time signal processing places stringent demands on DSP hardware and software design to complete predefined tasks within a certain time frame. The basic functional blocks of realrealtime DSP system is as illustrated below: x(t) Anti-aliasing x’(t) ADC filter Amplifier Input channels y(t) Event Name Reconstruction filter Other digital systems DSP hardware Output channels Amplifier x(n) DAC y’(t) y(n) Other digital systems Definition of DSPs Digital Signal Processor (DSP): DSP is the processing of signals by digital means, it is the study of systems by changing or analyzing information which is measured as discrete sequence of numbers An electronic system that processes digital signals a special-purpose programmable microprocessor manipulate in real time a communications stream of large amounts of digital data to improve its quality or modify it in specific ways Changing or analyzing information that is measured as discrete sequences of numbers The representation, transformation, and manipulation of signals and the information they contain Impact of DSP on Modern Living Cellular/mobile telephony Speech and channel coding Voice and data processing Power management Multipath equaliztion Automotive Digital Audio Digital Radio Personal communication systems Active suspension Personal computer Sound cards Data storage and retrieval Error correction/concealment Multimedia Modems Event Name Digital audio Stereo and surround sound Audio equalization and mixing Electronic music Medical electronics Critical/intensive care monitors Digital X-rays ECG analyzers Cardiac monitors Medical imaging REAL WORLD SIGNAL PROCESSING Example of DSPs: MP3 Player The DSP performs the MP3 encoding and saves the file to memory. the DSP would perform other functions such as volume control, equalization and user interface. Event Name Inside of DSPs A DSP contains these key components: Program Memory: Stores the programs the DSP will use to process data Data Memory: Stores the information to be processed Compute Engine: Performs the math processing, accessing the program from the Program Memory and the data from the Data Memory Input/Output: Serves a range of functions to connect to the outside world Overlapping Fields of DSP Communication Theory Digital Signal Processing Numerical Analysis Probability and Statistics Analog Electronics Event Name Digital Electronics Decision Theory Analog Signal Processing What is a signal? signal: is the variation of any measurable quantity that conveys information concerning the behaviors of related system A function of independent variables such as time, distance, position, temperature, pressure, etc. A signal carries information Examples: speech, music, seismic, image and video A signal can be a function of one, two or N independent variables Speech is a 1-D signal as a function of time 1An image is a 2-D signal as a function of space 2Video is a 3-D signal as a function of space and 3time Classification of Signals Signal CT x(t) Continuous Time Signal Discrete Time Signal DT x[n] Deterministic Periodic Sinusoidal Random Aperiodic Stationary Complex Ergotic Almost Periodic Event Name Non-stationary Transient Non-ergotic Definitions … Digital : operating by the use of discrete signals to represent data in the form of numbers Processing to perform operations on data according to programmed instructions System is a mathematical model or abstraction of physical process that relates input to the output or response of a system Definitions …. Discrete : the study of the system in which the time variable is defined only for discrete instant of time tk (where k is an integer). Continuous : where the input, output are all function of continuous real variable t. Event Name More Example Signals position EEG Stock price & volume time DTMF Video time time Types of Signals Analog Signals (Continuous-Time Signals) Signals that are continuous in both the dependant and independent variable (e.g., amplitude and time). Most environmental signals are continuous-time signals. Discrete Sequences (Discrete-Time Signals) Signals that are continuous in the dependant variable (e.g., amplitude) but discrete in the independent variable (e.g., time). They are typically associated with sampling of continuous-time signals. Event Name Continuous & Discrete-Time Signals Continuous-Time Signals ContinuousMost signals in the real world are continuous time, as the scale is infinitesimally fine. Eg voltage, velocity, Denote by x(t), where the time interval may be bounded (finite) or infinite Discrete-Time Signals DiscreteSome real world and many digital signals are discrete time, as they are sampled E.g. pixels, daily stock price (anything that a digital computer processes) Denote by x[n], where n is an integer value that varies discretely Sampled continuous signal x[n] =x(nk) – k is sample time nk) x(t) t x[n] Continuous & Discrete-Time Signals Event Name n How is a Signal Represented? Mathematically, signals are represented as a function of one or more independent variables. variables. For instance a black & white video signal intensity is dependent on x, y coordinates and time t f(x,y,t) On this course, we shall be exclusively concerned with signals that are a function of a single variable: time f(t) t Example: Signals in an Electrical Circuit R vs + - vs (t ) − vc (t ) R dvc (t ) i (t ) = C dt 1 dvc (t ) 1 + vc (t ) = vs (t ) RC dt RC i (t ) = i C vc The signals vc and vs are patterns of variation over time vs, vc • Step (signal) vs at t=1 • RC = 1 • First order (exponential) response for vc t Note, we could also have considered the voltage across the resistor or the current as signals Event Name Signal Properties Periodic signals: a signal is periodic if it repeats itself after a signals: fixed period T, i.e. x(t) = x(t+T) for all t. A sin(t) signal is t+T) sin(t periodic. Even and odd signals: a signal is even if x(-t) = x(t) (i.e. it can be signals: x(t reflected in the axis at zero). A signal is odd if x(-t) = -x(t). Examples are cos(t) and sin(t) signals, respectively. cos(t sin(t Exponential and sinusoidal signals: a signal is (real) exponential signals: if it can be represented as x(t) = Ce at. A signal is (complex) exponential if it can be represented in the same form but C and a are complex numbers. Step and pulse signals: A pulse signal is one which is nearly signals: completely zero, apart from a short spike, d(t). A step signal is zero up to a certain time, and then a constant value after that time, u(t). These properties define a large class of tractable, useful signals signals and will be further considered in the coming lectures Processing Real Signals Most of the signals in our environment are analog such as sound, temperature and light, ..etc To processes these signals with a computer, we must: 1. convert the analog signals into electrical signals, e.g., using a transducer such as a microphone to convert sound into electrical signal 2. digitize these signals, or convert them from analog to digital, using an ADC (Analog to Digital Converter) Event Name Processing Real Signals (cont.) In digital form, signal can be manipulated Processed signal may need to be converted back to an analog signal before being passed to an actuator (e.g., a loudspeaker) Digital to analog conversion and can be done by a DAC (Digital to Analog Converter) Typical DSP System Components Input lowpass filter (anti-aliasing filter) Analog to digital converter (ADC) Digital computer or digital signal processor Digital to analog converter (DAC) Output lowpass filter (anti-imaging filter) Event Name DSP System Components Analog input signal is filtered to be a bandlimited signal by an input lowpass filter Signal is then sampled and quantized by an ADC Digital signal is processed by a digital circuit, often a computer or a digital signal processor Processed digital signal is then converted back to an analog signal by a DAC The resulting step waveform is converted to a smooth signal by a reconstruction filter called an anti-imaging filter Digital Signal Processing Digital processing of analog signal Conversion of analog signal to digital form Processing of the digital version Conversion of the processed digital signal into analog Main element in DSP system Sampler Quantizer DSPs Decoder Event Name Digital Signal processing Advantages of DSP Versatility Digital systems can be reprogrammed for other applications Digital systems can be ported to different hardware Repeatability and stability Digital systems can be easily duplicated Digital systems do not depend on strict component tolerances Digital system responses do not drift with temperature Cost In some cases, implementations in DSP is cheaper and more reliable than analog systems Storage Easily to store on magnetic media (CD, Tap ..) with out losses Event Name Advantages of DSP (cont.) Simplicity Some things can be done more easily digitally than with analog systems (e.g., linear phase filters) Security can be introduced by encryption/scrambling Digital signals easily stored on magnetic media without deterioration Accuracy In DSP, accuracy is determined by the number of bits. This means that control requirement is better in DSP than in ASP Tolerance in analog systems leads to different control of accuracy Drift in performance No drift in performance with temperature or age Analog systems vary with temperature or age. Disadvantages of DSP DSP techniques are limited to signals with relatively low bandwidths The point at which DSP becomes too expensive will depend on the application and the current state of conversion and digital processing technology Currently DSP systems are used for signals up to video bandwidths (about 10 MHz) The cost of high-speed ADCs and DACs and the highamount of digital circuitry required to implement very high-speed designs (> 100 MHz) makes highthem impractical for many applications As conversion and digital technology improve, the bandwidths for which DSP is economical continue to increase Event Name Disadvantages of DSP (cont.) The need for an ADC and DAC makes DSP not economical for simple applications (e.g., a simple filter) Higher power consumption and size of a DSP implementation can make it unsuitable for simple very low-power or small size applications DSP Applications Image Processing Pattern recognition Robotic vision Image enhancement Facsimile Satellite weather map Animation Instrumentation/Control Spectrum analysis Position and rate control Noise reduction Data compression Speech/audio Speech recognition/synthesis Text to speech Digital audio equalization Event Name Military Secure communication Radar processing Sonar processing Missile guidance Telecommunications Echo cancellation Adaptive equalization ADPCM transcoders Spread spectrum Video conferencing Data communication Biomedical Patient monitoring Scanners EEG brain mappers ECG analysis X-ray storage/enhancement Example DSP Applications…. COMMUNICATIONS Echo Cancellation Digital PBXs Line Repeaters Modems Global Positioning Sound/Modem/Fax Cards Cellular Phones Speaker Phones Video Conferencing ATMs Wireless Local Loop Private Data Comms Systems VOICE/SPEECH DSP Spectrum Analyzers Seismic Processors Digital Oscilloscopes Mass Spectrometers Robotics Numeric Control Power Line Monitors Motor/Servo Control AV Editing Digital Mixers Home Theater Pro Audio CONSUMER INSTRUMENTATION INDUSTRIAL/CONTROL PRO-AUDIO PRO- Speech Recognition Speech Processing/Vocoding Speech Enhancement Text-to-Speech Voice Mail MEDICAL Patient Monitoring Ultrasound Equipment Diagnostic Tools Fetal Monitors Life Support Systems Image Enhancement Radar Detectors Power Tools Digital Audio / TV Music Synthesizers Toys / Games Answering Machines Digital Speakers MILITARY Secure Communications Sonar Processing Image Processing Radar Processing Navigation, Guidance Examples of DSP Applications Communication: encoding and decoding of digital communication signals, detection, equalization, filtering, direction finding, echo cancellation Radar and Sonar: target detection, position and velocity estimation, tracking Biomedical Engineering: analysis of biomedical signals, diagnosis, patient monitoring, preventive health care, artificial organs Event Name Why do we need DSP processors? Why not use a General Purpose Processor (GPP) such as a Pentium instead of a DSP processor? What is the power consumption of a Pentium and a DSP processor? What is the cost of a Pentium and a DSP processor? Why do we need DSP processors? Use a DSP processor when the following are required: Cost saving. Smaller size. Low power consumption. Processing of many “high” frequency signals high” in real-time. realUse a GPP processor when the following are required: Large memory. Advanced operating systems. Event Name CPU & DSP generalist. Specialist orchestrate the operation of diverse pieces of computer hardware modify the numbers in a digital signal stream—and do it stream— quickly. modern CPU several hundred instructions instruction set much smaller ,no more than 80 instructions. a typical desktop microprocessor contains tens of millions of transistors. consists of several hundred thousand transistors. DSP Historical Perspective • Nyquist Theorem 1920's • Statistical Time Series, PCM 1940's • Digital Filtering, FFT, Speech Analysis mid 1960s (MIT, Bell Labs, IBM) • Adaptive Filters, Linear Prediction (Stanford, Bell Labs, Japan 1960s) • Digital Spectral Estimation, Speech Coding (1970s) Event Name DSP Historical Perspective (cont.) • First Generation DSP Chips (Intel microcontroler, TI, AT&T, Motorola, Analog Devices (early 1980s) • Low-cost DSPs (late 1980s) • Vocoder Standards for civilian applications (late 1980s) • Migration of DSP technologies in general purpose CPU/Controllers "native" DSP (1990s) • High Complexity Rich Media Applications • Low Power (Portable) Applications History of DSP (cont.) 1965: Cooley and Tukey (re)discover efficient algorithm for Fast Fourier Transforms (FFTs) – made feasible real-time signal processing as well as algorithms previously thought impossible to implement on digital computers 1980’s: IC technology advancements led to very fast fixed-point and floating-point microprocessors for digital signal processing Event Name Historical Perspective of DSP Fast Fourier Transform (FFT) Signal processing with analog system & digital computer Microelectronics in VLSI technology Numerical methods IC technology DSP chips Calculus 1600’s 1700’s 1950’s 1965 1980’s Nanotechnology 1990’s Future DSP Functions Common features of DSP applications They use a lot of multiplying and adding operations They deal with signals that come from the real world They require a certain response time Key DSP operations Filtering Correlation Discrete transformation Event Name Filtering Example Signals are usually a mix of “useful” information and noise How do we extract the useful information? Filtering is one way Filtering Example (cont.) Event Name Filtering Equations Let x[n] denote current input value (ECG+noise) x[n-1] is previous input value, x[n-k] – k-th previous input x[nx[nLet y[n] be the current filtered output value y[n-1] is previous output value , y[n-k] – k-th previous y[ny[noutput Filtering operations carried out for this example: y[n] = 2.4*y[n-1] - 2.6*y[n-2] + 1.5 y[n-3] – 0.4*y[n-4] 2.4*y[n2.6*y[ny[n0.4*y[n+ 0.6*x[n] – 1.9*x[n-1] + 2.8*x[n-2] 1.9*x[n2.8*x[n- 1.9*x[n-3] + 0.6*x[n-4] 1.9*x[n0...
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