arm processor
DESCRIPTION
its a presentation on arm processor which can help in the prijectTRANSCRIPT
RTOS PORTING ON SMALL FOOT PRINT ARM PROCESSOR AND IMPLEMENTATION OF
NETWORK PROTOCOL
BySAMADHAN D. MALI
Under the Guidance ofPROF. Dr. AJAY D. JADHAV
Department of E&TC
Sinhgad College of Engineering
ContentsReview of Theories ObjectiveIntroductionExperimentationResults & DiscussionsConclusionPublicationsReferences
Review of TheoriesRTOS
Real Time Operating SystemDeterministic NatureServices
Task ManagementIntertask Communication and SynchronizationI/O SupervisorDynamic Memory AllocationTimers
Review of TheoriesRTOS Scheduler
1. Non-preemptive scheduling
ISRISR makes high priority task ready
Low priority task relinquishes CPU
Low priority task
High priority task
2. Preemptive scheduling
ISR
High priority task relinquishes CPU
ISR makes highpriority task ready
Low priority task
High priority
task
Review of Theories
Review of TheoriesNETWORK PROTOCOLS
MODBUS PROTOCOL Present at Second level of ISO-OSI Model It’s a Serial Line Master-Slave Protocol Query/Response Mode of Communication
Review of Theories Message Frame Structure
ObjectiveDesigning embedded system has
constraints like memory space, energy consumption, reliability, performance, execution time of an application, small size and capability to upgrade software.
In the view of designing embedded system application where the scheduling of multiple tasks is required, the real time operating system is generally used as abstraction layer between hardware and application software. To achieve this, it aimed to design an ARM processor based system for implementation of MODBUS protocol integrating with RTOS µCOS-II.
Block Diagram of the Project
Introduction
Introduction
Basic structure of circular microstrip antenna is
Present Theories
Microstrip antennas have Narrow bandwidth Poor polarization purity
Problem Statement
ObjectivesTo enhance the bandwidth of circular
microstrip antenna.
To design circularly polarized circular microstrip antenna
Methodology (Detailed)
1. Bandwidth Enhancement Techniques1. Modification of shape
2. Use of stacked structure
3. Use of parasitic patches
CMSA with Two Notches CMSA using Stacked Structure
Use of Planer Multiresonator
Patches
PolarizationOrientation of the electric field (E-plane) of the radio
wave with respect to the earth's surface
Linear Polarization- Only one component presentCircular Polarization
When Ex0 = Ey0 and 900 phase difference between them.Measured in terms of Axial Ratio
Otherwise wave is elliptically polarized. 𝐴𝑅= 𝑚𝑎𝑗𝑜𝑟 𝑎𝑥𝑖𝑠𝑚𝑖𝑛𝑜𝑟 𝑎𝑥𝑖𝑠
Circular Polarization (CP)Advantages
No requirement of alignment between transmitter and receiver antenna
After reflection from metallic objects, sense of polarization reverses, so useful in RADAR, navigational systems
Techniques to produce CP
Dual Feed Single Feed
Conditions for CP1. E-field must have two orthogonal components
2. These components should have equal magnitude
3. Orthogonal components must be 900 out of phase
Methodology for CP (Selected) Modification of shape
CMSA with two notches
Design of Simple CMSAFor Simple CMSA
Effective radius (ae ) of the patch in CMSA is calculated as
Where Knm : mth zero of first order derivative of Bessel’s function ≈ 1.84118
c : Velocity of light
f0 : Resonant frequency
ɛe : Effective dielectric constant
But actual patch radius is required, which can be calculated as
Where
a : actual radius
h : Height of dielectric substrate
ɛr : Dielectric constant
Design of Simple CMSA
Specifications : Input
1. Dielectric constant= 4.4 (Material chosen- Glass Epoxy)
2. Height of substrate= 1.6 mm Output
1. Resonant Frequency = 2.45 GHz
2. Bandwidth >= 2%
3. Gain ≈ 2 dBi
Design of Simple CMSA
Steps1. For given input parameters, effective patch radius is
ae = 17.1 mm
2. From ae , actual patch radius is
a= 16.6 mm
Design of Simple CMSA
Design of CMSA for CP
Change in geometry by amount ∆𝑆
For minimum AR
Where K11 = 1.84118
∆𝑆𝑆 = 0.543𝑄0
Δ𝑆𝑆 ∗𝑄0 ≈ 1𝐾11
Design of CMSA for CP
Q0 can be calculated from
For our design
Q0 ≈ 46.15 but
So
ℎ𝜆0 =0.013
𝑄0ሺ𝜀𝑟ሻ= 𝑄0 ∗ξ𝜀𝑟ξ2.55
Q0 (𝜀𝑟) = 60.62
Design of CMSA for CP
So now
But
This is perturbation area.
∆𝑆𝑆 = 8.956e-3
𝑆= 𝜋∗𝑎2 𝑆= 8.657e-4
∆𝑆=7.753e-6≈ 8 mm2
Design of CMSA for CP
Design Parameters for Modified CMSA
Antenna Parameters
Circular patch radius
Coaxial feed position
Coaxial feed probe radius
Perturbation Area Length
Perturbation Area Width
OptimizationDetermining best course of action amongst different
alternatives available in decision making.
ORProcess of finding optimal value of objective function
under given set of constraintsPhases in optimization
1. Modelling
2. Solution of mathematical model
3. Validation of results
If results are not up to the mark
Need of OptimizationIn EM, equations are highly nonlinear and complexLarge number of optimization parametersOutput parameters depends upon complex relations
between input parameters.
Out of available combinations of input parameters, which would be best?
Optimization Techniques
Random Search TechniquesSimulated AnnealingGenetic AlgorithmFuzzy LogicArtificial Neural Network
Random Search Based TechniquesGenerates random sequence of input parameters and
returns best point
….where value of objective function is lowest
Not suitable if parameters are many And if no fix relation between input parameters
Simulated AnnealingIn each generation, current solution changed randomly.
X -> X’∆f=f(X)-f(X’)If ∆f<=0, Change is acceptedIf ∆f>=0, change is accepted with conditionsDisadvantages
Quality of solutions is not stable..Computational time is quite long.
Genetic Algorithm
Globally accepted parameter optimization techniqueBased on Darwin’s concept
“Survival of the fittest”GA’s are known as the best approach if the number of
unknown parameters increases.
Algorithm in Detail…….
Establish encoding/decoding of parameters
Generate M random chromosomes
Evaluate cost function for the chromosomes
Rank chromosomes
Discard inferior chromosomes
Mate remaining chromosomes
Done? Mutations
Stop
Start
No
Yes
• Very slow search speedDue to selection of inappropriate parameters• Premature convergenceParameters always remain fixed irrespective of operating environment
Fuzzy LogicGeneralization of Boolean logic implementing the
concept of partial truth or uncertainty.Fuzzy logic is conceptually easy to understand.Fuzzy logic can be built on top of the experience of
experts.Not so effective when physical processes relationships are
not fully understood.
Artificial Neural NetworkA computational model inspired from Human
Nervous System.Learns from examplesConsists of an interconnected group of artificial
neuronsProcesses information using a connectionist approach
to computation.Resembles human brain in following aspects
1. Knowledge acquisition through learning process
2. Interneuron connection strengths used to store knowledge
A Simple Artificial Neural Net
x1
x2
y2
Input Layer Output Layer
W1 (Weights)
W2x2
x1
Inpu
ts
(Output)
Why ANN ?Massive parallelismDistributed representation and computationAbility and adaptability to learnGeneralization ability Expertise in perceptual problemsEase of implementation
ANN BasicsTypes of ANN
Feed BackFeed forward CompetitiveRecurrent
Training Methods1. Supervised 2. Unsupervised3. Reinforcement
ANN BasicsTypes of ANN
Feed BackFeed forward CompetitiveRecurrent
Training Methods
1. Supervised 2. Unsupervised3. Reinforcement
Multilayer Perceptron…….Feed forward network with supervised learningGoal of this type of network is to create a model that
correctly maps the input to the output using historical data
Single Hidden Layer Multiplayer Perceptron Network
Input Parameters
Hidden Layer
Output Parameters
Learning of Multilayer Perceptron
Back Propagation Algorithm
Application of ANN to Project
Input and Output Parameters
Sr. No. Input Parameters Output parameters
1 Circular patch radius Bandwidth
2 Coaxial feed position Resonant Frequency
3 Coaxial feed probe radius Axial ratio
4 Perturbation Area Length Antenna Efficiency
5 Perturbation Area Width Gain
ANN Models (Synthesis)
Artificial
Neural
Network
Model
Patch Radius
Feed Location
Feed Probe Radius
Perturbation Area Length
Perturbation Area Width
Bandwidth
Resonant Frequency
Axial Ratio
Antenna efficiency
Gain
ANN Models (Analysis)
Artificial
Neural
Network
Model
Patch Radius
Feed Location
Feed Probe Radius
Perturbation Area Length
Perturbation Area Width
Bandwidth
Resonant Frequency
Axial Ratio
Antenna efficiency
Gain
Before Using ANNWe should decide1. Number of input and outputs in single training pair
2. Total number of training pairs
3. Number of hidden layers and neurons in each hidden layer
Design Considerations for ANN
Artificial
Neural
Network
Model
Patch Radius
Feed Location
Feed Probe Radius
Perturbation Area Length
Perturbation Area Width
Bandwidth
Resonant Frequency
Axial Ratio
Antenna efficiency
Gain
Synthesis ANN Model
1. Number of inputs and outputs in 1 training pair
Design Considerations for ANN
2. Total number of training pairs
Database for ANN
Input parameter and no. of combinations
Parameter No. of combinations
Patch radius 3
Parameter Initial value Final value Step
Patch radius 16.55 mm 16.65 mm 0.5 mm
Actual values considered
Database for ANN
Input parameter and no. of combinations
Parameter No. of combinations
Feed position 3
Parameter Initial value Final value Step
Patch radius 5 mm 6 mm 0.5 mm
Actual values considered
Database for ANNInput parameter and no. of combinations
Parameter No. of combinations
Feed probe radius 4
Parameter Initial value Final value Step
Patch radius 0.5 mm 0.8 mm 0.1 mm
Actual values considered
Database for ANNInput parameter and no. of combinations
Parameter No. of combinationsLength and width of Perturbation Area
4
Actual values considered
Sr. No.Length of P. A.
(in mm)Width of P. A.
(in mm)
1 1 4
2 2 2
3 3 1.33
4 4 1
Database for ANN
Total combinations
Total combinations- (3*3*4*4)=144
Parameter No. of combinations
Patch radius 3
Feed position 3
Feed probe radius 4
Length and width of P. A. 4
Total Number of Training Pairs=144
Database
Design Considerations for ANN
2. Total number of training pairs = 98 Validation Samples= 21 Test Samples= 21
3. Number of Hidden Layers and
Number of Neurons in Each Hidden Layer
Feed forward network
Input Parameters
Hidden Layer
Output Parameters
Design Considerations for ANN
Training ParametersNo. of epochs- 5000 (max)Goal = 1e-5
Maximum Number of weights (w) = 95
Design Considerations for ANN
3. Number of Neurons in Each Hidden Layer
Structure of Input Layer
5 weights for 1 neuron; 5 Neurons in I/P Layer;Total Weights = 25
Structure of Hidden Layer
5 neurons in previous layer; 7 Neurons in Hidden Layer;Total Weights = 35
Structure of Output Layer
7 neurons in previous layer; 5 Neurons in O/P Layer;Total Weights = 35
Structure of Total Network
Input Layer Hidden Layer Output Layer
Performance (Synthesis ANN Model)
Sr. No.
No. of Neuronsin Hidden
Layer
No of Epochs
Required
Average Percentage Deviation
Training Validation Testing
1 7 48 0.078 0.7 0.61
2 6 33 0.2042 0.3638 1.088
3 5 30 0.0595 0.1342 0.5667
4 4 87 0.0802 0.8796 0.7787
5 2 13 1.0872 4.3304 5.1172
Forward Training, Validation and Testing
Sr. No.
ParameterInput Vector
TargetVector
Output VectorGiven by ANN
Percentage Deviation
1 Patch Radius 16.55 89 88.4 0.7068
2 Feed position 5 2.43 2.43 0.0037
3 Feed Probe Radius 0.7 3.07 3.00 2.1737
4 Length of P.A. 3 36.37 36.47 0.2839
5 Width of P.A. 1.33 1.956 1.993 1.9086
Average Percentage Deviation=0.1384
Sample Number: 75
Results (Synthesis ANN Model)
Artificial
Neural
Network
Model
Patch Radius
Feed Location
Feed Probe Radius
Perturbation Area Length
Perturbation Area Width
Bandwidth
Resonant Frequency
Axial Ratio
Antenna efficiency
Gain
Performance (Analysis ANN Model)
Sr. No.
No. of Neuronsin Hidden
Layer
No of Epochs
Required
Average Percentage Deviation
Training Validation Testing
1 7 29 0.1763 0.4520 0.7160
2 6 24 0.5362 0.7756 1.0259
3 5 15 0.7711 1.1166 1.4653
4 4 38 0.6816 0.8573 1.2640
5 2 35 1.1512 4.0969 5.0671
Reverse Training, Validation and Testing
Sr. No.
ParameterInput Vector
TargetVector
Output VectorGiven by ANN
Percentage Deviation
1 Bandwidth 81 16.65 16.62 0.4225
2 Resonant Freq. 2.427 5.5 5.425 1.3712
3 Axial Ratio 3.08 0.8 0.8376 4.669
4 Efficiency 35.19 3 2.994 0.2125
5 Gain 1.81 1.33 1.331 0.0612
Results (Analysis ANN Model)
Average Percentage Deviation= 1.3533
Sample Number: 80
Artificial
Neural
Network
Model
Patch Radius
Feed Location
Feed Probe Radius
Perturbation Area Length
Perturbation Area Width
Bandwidth
Resonant Frequency
Axial Ratio
Antenna efficiency
Gain
Sr. No.
PerformanceParameter
Desired value
Physical parameter
Result given by ANN ( in mm )
1Bandwidth 110 MHz Patch Radius 16.48
2Resonant Frequency 2.45 GHz Feed position 5.93
3Axial Ratio 1.2 Feed Probe Radius 0.4075
4Antenna Efficiency 36 % Length of P.A. 4.09
5Gain 2 Width of P.A. 0.9032
Results (Analysis ANN Model)
Results Given by IE3D Model
Parameter Expected value
Simulated value Percentage Deviation
Bandwidth 110 MHz 112 MHz -1.81
Resonant frequency 2.45 GHz 2.449 GHZ 0.04
Axial ratio 1.2 1.26 -0.05
Antenna efficiency 36 % 36.66 % -1.833
Gain 2 2.02 1
Average Percentage Deviation= 0.9446
SimulationSoftware chosen
IE3D by Zeland® corporationBased on method of momentsHigh efficiency, high accuracy and low cost
electromagnetic simulation tool on PCs with Windows based graphic interface.
Built-in library for construction of complicated structures, such as circles, rings, spheres, rectangular.
3D and 2D display of current distribution, radiation patterns
Simulation of simple CMSA
Simple CMSA geometry in IE3D MGrid
Results of Simple CMSA
S11 vs Frequency
CMSA S11 (minimum)
Resonant Frequency
Simple -23 dB 2.449 GHz
VSWR vs Frequency
Results of Simple CMSA
CMSA VSWR
Simple 1.149
Smith Chart
Results of Simple CMSA
Resonant
Frequency
(in GHz)
Bandwidth( in MHz)
Bandwidth(in %)
AxialRatio
Ant.Efficiency
Gain
2.449 56 2.28 46.5 37.55 2.09
Results of Simple CMSA
Simulation Results Comparison(Simple CMSA & Modified CMSA)
CMSA VSWR
Simple 1.149
Modified 1.06
VSWR Plot
Simulation Results Comparison(Simple CMSA & Modified CMSA)
CMSA S11 (minimum)
Resonant Frequency
Simple -23 dB 2.449 GHz
Modified -30.71 dB 2.449 GHz
S11 Plot
Simulation Results Comparison(Simple CMSA & Modified CMSA)
Smith Chart Plot
Simple CMSA Modified CMSA
Simulation Results Comparison(Simple CMSA & Modified CMSA)
Axial Ratio Plot
1.26
3 dB Bandwidth= 31 MHz
Simple CMSA Modified CMSA
Simulation Results Comparison(Simple CMSA & Modified CMSA)
37.55 %
Antenna Efficiency Plot
36.99 %
Simple CMSA Modified CMSA
Simulation Results Comparison(Simple CMSA & Modified CMSA)
2.09
Antenna Gain Plot
2.02
Sr. No. Performance parameter Simple CMSA Modified CMSA
1 Bandwidth56 MHz (2.28%)
112 MHz (4.57%)
2Resonant Frequency 2.449 GHz 2.449 GHZ
3Axial Ratio 46.65 1.26
4Antenna Efficiency 37.55 % 36.99 %
5Gain 2.07 2.02
Simulation Results Comparison(Simple CMSA & Modified CMSA)
Experimental Results
Simple CMSA Modified CMSA
VSWR Plot
Bandwidth = 70 MHzfc = 2.47 GHz
Bandwidth= 140 MHzfc = 2.43 GHz
Experimental Results
Simple CMSA Modified CMSA
S11 Plot
Min. S11= -20 dB Min. S11 = -17.8 dB
Simulation and Experimental Result Comparison
Sr. No.
Performance Parameter
Simple CMSA Modified CMSASimulation
ResultExperimental
ResultSimulation
ResultExperimental
Result
1 Bandwidth (in MHz) 56 70 112 140
2 Bandwidth (in %) 2.28 2.83 4.57 5.76
3 Resonant Frequency (in GHz)
2.449 2.47 2.449 2.43
ConclusionBandwidth can be increased, almost double.Axial ratio is achieved near unity, so antenna can be said
as circularly polarized. No shift in resonant frequency of Modified CMSA
Slight reduction in antenna gain and efficiency.General model using ANN, for calculation of antenna
physical parameters is build and tested.Practical results are almost matching with simulated
results.
PublicationsSachin Takale, Dr. Shashikant Lokhande, “Design of Circular
Microstrip Antenna for Circular Polarization using ANN”, CiiT International Journal of Artificial Intelligence and Machine Learning, Nov. 2010, pp. 312-318 (Impact Factor – 0.765)
Sachin Takale, Dr. Shashikant Lokhande, “Design of single feed circularly polarized circular microstrip antenna”, National Conference on Pervasive Computing, NCPC-2010, April 9-10, 2010
Sachin Takale, Dr. Shashikant Lokhande, “Optimization of multilayered microstrip antenna using fuzzy-genetic approach”, e-PGCoN, April 28, 2008.
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Pvt. Ltd. 2005. pp. 1-17,722-7846.
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[3] Girish Kumar, K. P. Ray, “Broadband Microstrip Antennas” , Artech House, Norwood, MA. 2003
[4] S. N. Sivanandam, S. Sumathi, S. N. Deepa, “Introduction to Neural Networks using MATLAB 6.0”, Tata McGraw-Hill, 2006
[5] Howard Demuth, Mark Beale, “Neural Network Toolbox Version 4.0.4”, The MathWorks, Inc., Natick, MA, 2004.
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[7] David M Pozar, “ Microstrip Antennas”, Invited Paper, Proceedings of the IEEE, vol. 80, no. 1, pp. 79-91, January 1992.
[8] Keith R. Carver, James W. Mink, “ Microstrip Antenna Technology”, IEEE transactions on Antennas and Propagation vol. AP-29, no. 1, pp. 2-23, January 1981.
References[9] S.Devi, Dhruba C Panda and Shyam S Pattnaik, “ A novel method of using
Artificial Neural Networks to calculate input impedance of circular microstrip antenna”, IEEE, 2002.
[10] E. R. Brinhole1, J. F. Z. Destro, A. A. C. de Freitas, and N. P. de Alcantara Jr.,“Determination of Resonant Frequencies of Triangular and Rectangular Microstrip Antennas, using Artificial Neural Networks”, Progress In Electromagnetics Research Symposium 2005, Hangzhou, China, August 22-26, pp. 579-582
[11] V. V. Thakare and P. Singhal, “ Bandwidth Analysis By Introducing Slots In Microstrip Antenna Design Using ANN, Progress In Electromagnetics Research M, Vol. 9, 107-122, 2009.
[12] V. V. Thakare and P. Singhal, “Neural network Based CAD model for the design of rectangular patch antennas”, Journal of Engineering and Technology Research Vol.1 (7), pp. 129-132, October, 2009
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