Microcomputer Systems 1 Digital Systems: Hardware Organization and Design 11/19/2018 Microcomputer Systems 1 Introduction to DSP’s Architecture of a Respresentative 32 Bit Processor
Digital Systems: Hardware Organization and Design 11/19/2018 Introduction to DSP’s Definition: DSP – Digital Signal Processing/Processor It refers to: Theoretical signal processing by digital means (subject of ECE3222, ECE3541), Specialized hardware (processor) that can process signals in real-time (subject of this course ECE3551&2) This class’s focus is on: Hardware Architecture of a real-world DSP platforms Software Development on DSPs, and Applied Signal Processing theory. 19 November 2018 Veton Këpuska Architecture of a Respresentative 32 Bit Processor
Digital Systems: Hardware Organization and Design 11/19/2018 Introduction to DSP’s DSP’s process signals Signal – a detectable physical quantity or impulse (as a voltage, current, or magnetic field strength) by which messages or information can be transmitted (Webster Dictionary) 19 November 2018 Veton Këpuska Architecture of a Respresentative 32 Bit Processor
Digital Systems: Hardware Organization and Design 11/19/2018 Introduction to DSP’s Signal Characteristics: Signals are Physical Quantities: Signals are Measurable Signals are Analog Signals Contain Information. Examples: Temperature [oC] Pressure [Newtons/m2] or [Pa] Mass [kg] Speed [m/s] Acceleration [m/s2] Torque [Newton*m] Voltage [Volts] Current [Amps] Power [Watts] In this class, analog signals are electrical. Sensors: are devices that convert other physical quantities (temperature, pressure, etc.) to electrical signals. 19 November 2018 Veton Këpuska Architecture of a Respresentative 32 Bit Processor
Introduction to Signals and Systems Signal and Systems Introduction to Signals and Systems
Modeling Engineers model two distinct physical phenomena: Signals are modeled by mathematical functions. Physical systems are modeled by mathematical equations. 19 November 2018 Veton Këpuska
Introduction to Signals and Systems Introduction to Signals and Systems as related to Engineering Modeling of physical signals by mathematical functions Modeling physical systems by mathematical equations Solving mathematical equations when excited by the input functions/signals. 19 November 2018 Veton Këpuska
What are Signals? 19 November 2018 Veton Këpuska
Signals Signals, x(t), are typically real functions of one independent variable that typically represents time; t. Time t can assume all real values: -∞ < t < ∞, Function x(t) is typically a real function. 19 November 2018 Veton Këpuska
Example of Signals: Speech 19 November 2018 Veton Këpuska
Another view: 19 November 2018 Veton Këpuska
Speech Signal 19 November 2018 Veton Këpuska
1.5 second of Speech 19 November 2018 Veton Këpuska
Example of Signals EKG: 19 November 2018 Veton Këpuska
Example of Signals: EEC 19 November 2018 Veton Këpuska
Categories of Signals Signals can be: Continuous, or Discrete: T – sampling rate f – sampling frequency – 1/T – radial sampling frequency – 2f= 2/T 19 November 2018 Veton Këpuska
Corrupting, stochastic noise signal Signal Processing Signals are often corrupted by (additive) noise. s(t) = x(t)+n(t) Want to ‘filter’ the measured signal s(t) to remove undesired noise effects n(t). Need to retrieve x(t). Deterministic signal Corrupting, stochastic noise signal Signal Processing 19 November 2018 Veton Këpuska
What is a System? 19 November 2018 Veton Këpuska
Modeling Examples Human Speech Production is driven by air (input signal) and produces sound/speech (output signal) Voltage (signal) of a RLC circuit Music (signal) produced by a musical instrument Radio (system) converts radio frequency (input signal) to sound (output signal) 19 November 2018 Veton Këpuska
Speech Production Human vocal tract as a system: Driven by air (as input signal) Produces Sound/Speech (as output signal) It is modeled by Vocal tract transfer function: Wave equations, Sound propagation in a uniform acoustic tube Representing the vocal tract with simple acoustic tubes Representing the vocal tract with multiple uniform tubes 19 November 2018 Veton Këpuska
Speech Communication Pathway 19 November 2018 Veton Këpuska
Anatomical Structures for Speech Production 19 November 2018 Veton Këpuska
Digital Systems: Hardware Organization and Design 11/19/2018 Uniform Tube Model Volume velocity, denoted as u(x,t), is defined as the rate of flow of air particles perpendicularly through a specified area. Pressure, denoted as p(x,t), and 19 November 2018 Veton Këpuska Architecture of a Respresentative 32 Bit Processor
RLC Circuit Voltage, v(t) input signal Current, i(t) output signal Inductance, L (parameter of the system) Resistance, R (parameter of the system) Capacitance, C (parameter of the system) 19 November 2018 Veton Këpuska
Newton’s Second Law in Physics The above equation is the model of a physical system that relates an object’s motion: x(t), object’s mass: M with a force f(t) applied to it: f(t), and x(t) are models of physical signals. The equation is the model of the physical system. 19 November 2018 Veton Këpuska
What is a System? A system can be a collection of interconnected components: Physical Devices and/or Processors We typically think of a system as having terminals for access to the system: Inputs and Outputs 19 November 2018 Veton Këpuska
Example: Single Input/Single Output (SISO) System + + Electrical Network Vin Vout - - Multiple Input/Multiple Output (MIMO) System x1 (t) y1 (t) x2 (t) System y2 (t) … … xp (t) yp (t) 19 November 2018 Veton Këpuska
Example: Alternate Block Diagram Representation of a Multiple Input/Multiple Output (MIMO) System x(t) y(t) System 19 November 2018 Veton Këpuska
System Modeling Physical System Mathematical Model Model Analysis Design Procedure Model Simulation 19 November 2018 Veton Këpuska
Model Types Input-Output Description State-Space Description Frequency-Domain Representations: Transfer Function - Typically used on ideal Linear-Time-Invariant Systems Fourier Transform Representation Time-Domain Representations Differential/Difference Equations Convolution Models State-Space Description Time-Domain Representation 19 November 2018 Veton Këpuska
Model Types Continuous Models Discrete Models 19 November 2018 Veton Këpuska
Digital Systems: Hardware Organization and Design 11/19/2018 Introduction to DSP’s Analog Continuous Not of interest Discrete Models: DSP process digital signals: Analog-to-Digital Converter (ADC) Binary representation of the analog signal Digital-to-Analog Converter (DAC) Digital representation of the signal is converted to continuous analog signal. 19 November 2018 Veton Këpuska Architecture of a Respresentative 32 Bit Processor
Analog Low-pass Filter ADC a) Continuous Signal b) Amplitude Quantized Signal fs x(t) Analog Low-pass Filter Sample and Hold xa(nT) Quantizer DSP x[n] c) Amplitude & Time Quantized – Digital Signal 19 November 2018 Veton Këpuska
Digital Systems: Hardware Organization and Design 11/19/2018 Example of ADC 19 November 2018 Veton Këpuska Architecture of a Respresentative 32 Bit Processor
Analog Low-pass Filter DAC a) Digital Output Signal b) Analog Signal c) Continuous Low-pass filtered Signal DSP Digital to Analog Converter Analog Low-pass Filter y(t) y[n] ya(nT) 19 November 2018 Veton Këpuska
Why Processing Signals? Digital Systems: Hardware Organization and Design 11/19/2018 Why Processing Signals? Extraction of Information Amplitude Phase Frequency Spectral Content Transform the Signal FDMA (Frequency Division Multiple Access) TDMA (Time Division Multiple Access) CDMA (Code Division Multiple Access) Compress Data ADPCM (Adaptive Differential Pulse Code Modulation) CELP (Code Excited Linear Prediction) MPEG (Moving Picture Experts Group) HDTV (High Definition TV) Generate Feedback Control Signal Robotics (ASIMOV) Vehicle Manufacturing Process Control Extraction of Signal in Noise Filtering Autocorrelation Convolution Store Signals in Digital Format for Analysis FFT … 19 November 2018 Veton Këpuska Architecture of a Respresentative 32 Bit Processor
Digital Telephone Communication System Example: Digital Systems: Hardware Organization and Design 11/19/2018 Digital Telephone Communication System Example: 19 November 2018 Veton Këpuska Architecture of a Respresentative 32 Bit Processor
Typical Architecture of a DSP System Digital Systems: Hardware Organization and Design 11/19/2018 Typical Architecture of a DSP System Analog Signal Processing Analog Signal Conditioning Sensor Digital Signal Processing Digital Signal Conditioning ADC DSP DAC 19 November 2018 Veton Këpuska Architecture of a Respresentative 32 Bit Processor
SASE_Lab Example 19 November 2018 Veton Këpuska
SASE_Lab Example (cont.) 19 November 2018 Veton Këpuska
SASE_Lab Example (cont.) 19 November 2018 Veton Këpuska
Digital Systems: Hardware Organization and Design 11/19/2018 Why Using DSP? Low-pass Filtering example: Analog Filter: Chebyshev Type I and Order 6, vs. Digital Filter: FIR 129-Tap Filter 19 November 2018 Veton Këpuska Architecture of a Respresentative 32 Bit Processor
Analog Filter: Chebyshev of Type I Digital Systems: Hardware Organization and Design 11/19/2018 Analog Filter: Chebyshev of Type I Chebyshev Type I (Pass-Band Ripple) 6-Pole 1.0 dB Pass-Band Ripple Non-liner Phase MATLAB: fdatool Order = 6 Fs = 10,000 Hz Fpass = 1,000 Hz Apass = 1 [dB] 19 November 2018 Veton Këpuska Architecture of a Respresentative 32 Bit Processor
Example of a 3-rd order Active low-pass filter implementation Digital Systems: Hardware Organization and Design 11/19/2018 Example of a 3-rd order Active low-pass filter implementation 19 November 2018 Veton Këpuska Architecture of a Respresentative 32 Bit Processor
Magnitude Response of Chebyshev Filter Type I Order 6. Digital Systems: Hardware Organization and Design 11/19/2018 Magnitude Response of Chebyshev Filter Type I Order 6. 19 November 2018 Veton Këpuska Architecture of a Respresentative 32 Bit Processor
Digital Systems: Hardware Organization and Design 11/19/2018 Pass-Band Ripple 1.0 dB 19 November 2018 Veton Këpuska Architecture of a Respresentative 32 Bit Processor
Digital Systems: Hardware Organization and Design 11/19/2018 Digital Filter Design FIR, 129-Tap, Less then 0.002 dB Pass Band Ripple Linear Phase 19 November 2018 Veton Këpuska Architecture of a Respresentative 32 Bit Processor
FIR Filter Magnitude Response Digital Systems: Hardware Organization and Design 11/19/2018 FIR Filter Magnitude Response 19 November 2018 Veton Këpuska Architecture of a Respresentative 32 Bit Processor
Less then 0.002 dB Pass-Band Ripple Digital Systems: Hardware Organization and Design 11/19/2018 Less then 0.002 dB Pass-Band Ripple 19 November 2018 Veton Këpuska Architecture of a Respresentative 32 Bit Processor
Analog vs. Digital Implementations Digital Systems: Hardware Organization and Design 11/19/2018 Analog vs. Digital Implementations Analog Cons: Approximate Filter Coefficients Only standard components available Environment Temperature dependent Less accurate Can be used only for designed purpose Pros: Operate in real-time Digital (DSP) Cons: Real-time operation is dependent on the speed of processor and the complexity of problem at hand. Pros: Accurate Filter implementation to desired precision Operation independent on the environment. Flexible DSP’s can be reprogrammed. 19 November 2018 Veton Këpuska Architecture of a Respresentative 32 Bit Processor
DSP Implementation of the FIR Filter Digital Systems: Hardware Organization and Design 11/19/2018 DSP Implementation of the FIR Filter 129-tap digital filter requires 129 multiply-accumulates (MAC) Operation must be completed within sampling interval (1/Fs) to maintain real-time. Fs=10000Hz = 10kHz ⇒ 100 s ADSP-21xx family performs MAC process in single instruction cycle Instruction rate > 129/100 s = 1.3 MIPS ADSP-218x 16-bit fixed point series: 75 MIPS. 19 November 2018 Veton Këpuska Architecture of a Respresentative 32 Bit Processor
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