MATLAB and Simulink for Engineering Education

2009 The MathWorks, Inc.

Tabrez Khan Applications Engineer MathWorks India Pvt. Ltd tabrez.khan@mathworks.in

Agenda
Filter Design & Analysis Fixed Point Analysis Neural Network Design and Analysis Lunch Break Signal Processing, Communications and Video System Design Event-Driven and Discrete Event Simulation Implementation and Verification on Embedded Targets

Objective
Filter Design and Analysis

What is fixed point?

Binary representation of data with finite number of bits Finite word length arithmetic with a fixed number of fractional digits Optimal for machine, hard for humans to program
Sign bit
If signed

1st integer bit

Binary point

Number of fractional bits (3) Word length (8)

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Who is interested in fixed-point design?

People whose everyday tasks involve:

Designing, analyzing, and verifying fixed-point algorithms FPGA or ASIC Fixed-point DSP chip Microcontrollers

Designers whose hardware targets include:

Fixed-Point Data Types in MATLAB

Features fixed-point numeric object fi

Bit-true, fixed-point math in MATLAB Fixed-point algorithm development in M Natural MATLAB syntax

>> a=fi(pi, true, 8, 5); >> bin(a) 0 1 0 1 0 1 1 . 0 s 2 1 . 1/2 1/4 1/8 1/16 1/32 >> double(a) 3.15625
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Parts of a fi Object
>> a = fi(pi) a = 3.1416015625 DataTypeMode: Signed: WordLength: FractionLength: RoundMode: OverflowMode: ProductMode: MaxProductWordLength: SumMode: MaxSumWordLength: CastBeforeSum: Fixed-point: binary point scaling true 16 13 nearest saturate FullPrecision 128 FullPrecision 128 true

data

numerictype

fimath

Streamlined Floating-to-Fixed Conversion

Turn on logging mode Set data type override parameters Observe dynamic range of variables in your M-code Set the best fixed-point attributes to avoid overflow or underflow and large quantization errors

Introduction
Neural Networks

The network is adjusted, based on a comparison of the output and the target, until the network output matches the target.
Target
Neural Network including connections (called weights) between neurons

Input

Output

Compare

Adjust weights
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Neural Networks applications

Biotech

Cancer detection, Gene expression analysis, EEG and ECG analysis, Data Mining Market forecasting, Credit evaluation, Fraud detection, Real estate appraisal, Mortgage screening, Corporate bond rating, Portfolio trading program, Corporate financial analysis, Currency price prediction OCR, Handwriting recognition, Biometrics, Image and data compression, Fault detection and compensation, Nonlinear adaptive control, Process control, Robotics

Financial Services

Other

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Common Pain
Technology is complex and difficult to understand Steep Learning curve associated with learning and applying neural networks Solution : Mathworks offers Neural Networks Toolbox that allows users to use proven and tested built-in training algorithms and graphical tools thereby substantially reducing the effort and time needed to design, implement, evaluate, visualize and simulate neural networks.

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Design of Neural Networks

Basically comprises of following three steps: Initialization >> net=newff([0 2],[20 1],{tansig, purelin}, trainlm); Training >> net=train(net,P,T) Simulation >> a=sim(net,P)

Conversion to Simulink Block

Simulink model from trained network can be generated using the gensim(net) command
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Sampling Signals
Analog signal sources
Electromagnetic, audio, sonar, biomedical

Sampling

x( n) = xa (nTs )
discrete signal analog signal sample time

2009 The MathWorks, Inc.

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Aliasing
Shannon Sampling Theorem:
fs > 2 fM

fs > 2 fM
fs 2 fM

Original signal and sampled signal have same frequency

Sampled signal is aliased to half the original frequency

2009 The MathWorks, Inc.

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Signal Generation
Signals Create a time-base vector
>> t = [0:0.1:2];

Signal as function of time

>> x = sin(pi*t/2);

Useful MATLAB Functions Nonperiodic functions

ones, zeros

Periodic functions
sin, cos, square, sawtooth

2009 The MathWorks, Inc.

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Amplitude Modulation
Amplitude Modulation is a process where the amplitude of a carrier signal is altered according to information in a message signal. The frequency of the carrier signal is usually much greater than the highest frequency of the input message signal.

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AM Basic Definitions
The AM signal
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s (t ) = Ac [1 + k m(t )]cos ct
The modulating signal:

-5 0 01 02 03 04 05 06 07 08 09 0 .0 .0 .0 .0 .0 .0 .0 .0 .0 .1 1

m(t )
The Carrier Signal:

-1 0 01 02 03 04 05 06 07 08 09 0 .0 .0 .0 .0 .0 .0 .0 .0 .0 .1 1

c(t ) = Ac cos c t

-1 0 01 02 03 04 05 06 07 08 09 0 .0 .0 .0 .0 .0 .0 .0 .0 .0 .1

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Wireless Communication System Design

Physical layer design

Digital baseband design: Channel coding, modulation, spreading and filtering RF/Analog: Non-linearity, phase noise and noise figure Receiver algorithms: Carrier recovery, timing recovery, equalization Acknowledgement schemes, call processing, and simple protocols

MAC/Data link layer design

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Collect Blocks and Set Parameters

QPSK Transmitter QPSK Receiver

1. Drag and Drop Library Blocks into Simulink Model 2. Double Click on Blocks to Edit Block Parameters
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Run Simulation and Visualize Results

Start Simulation

Visualize Output

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Construct Receiver and Examine Received Bits

QPSK Transmitter QPSK Receiver

Error Rate Error Bits Received Bitsc

Received Signal

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Example: IEEE 802.11a System

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Stateflow Overview
Extend Simulink with a design environment for developing state machines and flow charts Design systems containing control, supervisory, and mode logic Describe logic in a natural and understandable form with deterministic execution semantic

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Modeling Performance of Transport and Networks Layers

SimEvents extends Simulink with discrete-event simulation Efficient, event-driven modeling of data packets passing through network of queues, servers, gates, and switches Connects to lower layers modeled in Simulink and Stateflow

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Communications protocol stack

Example Open Systems Interconnection OSI model from International Standards Organization (ISO)
7. Application

e.g. web browser

7. Application

UML: Transactional modeling languages Large scale applications programming

6. Presentation

e.g. Windows

6. Presentation

5. Session

e.g. NT login

5. Session

SimEvents: Discrete event

4. Transport
e.g. TCP

4. Transport

3. Network
e.g. IP

3. Network

Stateflow: State transition Comms Blockset: coding, modulation, etc. Signal Proc Blockset: Sampled data processing Simulink: Time domain

2. Data Link and Medium Access Control

e.g. WiFi MAC

2. Data Link and Medium Access Control

1. Physical

e.g. WiFi PHY

1. Physical

"0". RF or "analog front end" components and medium (the aether, copper or fiber) with impairments caused by both

Seven specialist layers in a protocol stack: Divide and conquer

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Some Image Processing Applications

Automotive Defense Biometrics Medical Diagnosis

Earth Sciences

Microscopy

Semiconductors

Drug Discovery

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Objective: Noise Filtering in the Video Stream Construct your model

through an intuitive block diagram Use Simulink Blocks to build your algorithm and model your system

View signals from block to block to determine the effectiveness of your model
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Objective: To Build Sobel Algorithm for Edge Detection

Input Image

Edges

2D Filter

(.)2
SQRT and Thresholding

2D Filter

(.)2

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The System Design Challenge

We design, simulate, and validate system models and algorithms in MATLAB and/or Simulink How can we implement and verify designs on DSPs and GPPs? How can we implement and verify designs on FPGAs and ASICs?
C HDL

MATLAB and Simulink Algorithm and System Design

MCU

DSP

FPGA

ASIC

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Embedded Software Design: TI Processors

Algorithm Development

Target Support Package TC2

System Design

MATLAB

Simulink
Real-Time Workshop Target Support Packages C/ASM Code

Stateflow

Real-Time Workshop Embedded Coder Target Support Package TC6

DSP Hardware

Embedded IDE Link CC

Compile & Link

Texas Instruments Code Composer Studio Environment Debug

Down load

C2000 DSP C5000 DSP C6000 DSP

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Model-Based Design Flow for Hardware Implementation

Requirements

Algorithmic Development

System Design and Simulation

Simulink Stateflow Embedded MATLAB

EDA Simulator Link

Simulink HDL Coder

Filter Design HDL Coder

Xilinx ISE Synplify Pro PrecisionRTL Altera Quartus II DesignCompiler

/ External Code VHDL,

Generic RTL VHDL / Verilog Digital Embedded Legacy Electronics Software Verilog C, HDL Simulation C++

Mentor Graphics ModelSim

Cadence Incisive Synopsys Discovery

FPGA

ASIC MCU Synthesis P&R

Integration

DSP

Test Environments

Leonardo FPGA ASIC

Continuous V&V 31

Implement Implement

MathWorks India Contact Details

E-mail: info@mathworks.in Technical Support E-mail: support@mathworks.in URL: http://www.mathworks.in Address / Tel/ Fax: MathWorks India Private Limited Salarpuria Windsor Building Third Floor, 'A' Wing No.3 Ulsoor Road Bangalore - 560042, Karnataka India Tel: +91 - 80 - 6632 6000 Fax: +91- 80 - 6632 6010

Thank You!
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