This work presents a Neural Network model for the design of Microstrip Antenna for a desired frequency between 3.5 GHz to 5.5 GHz. The results obtained from the proposed method are compared with the results of IE3D and are found to be in good agreement. The advantage of the proposed method lies with the fact that the various parameters required for the design of specific Microstrip antenna at a particular frequency of interest can be easily extracted without going into the rigorous time consuming, iterative design procedures using a costly software package. In this work, a general design procedure is suggested for the Microstrip antennas using artificial neural networks and this is demonstrated using the rectangular patch geometry.
Design & Study of Microstrip Patch Antenna.The project here provides a detailed study of how to design a probe-fed Square Micro-strip Patch Antenna using HFSS, v11.0 software and study the effect of antenna dimensions Length (L), and substrate parameters relative Dielectric constant (εr), substrate thickness (t) on the Radiation parameters of Bandwidth and Beam-width.
Design & Study of Microstrip Patch Antenna.The project here provides a detailed study of how to design a probe-fed Square Micro-strip Patch Antenna using HFSS, v11.0 software and study the effect of antenna dimensions Length (L), and substrate parameters relative Dielectric constant (εr), substrate thickness (t) on the Radiation parameters of Bandwidth and Beam-width.
An antenna array (or array antenna) is a set of multiple connected antennas which work together as a single antenna, to transmit or receive radio waves. The individual antenna elements are connected to a single receiver or transmitter by feedlines that feed the power to the elements in a specific phase relationship. The radio waves radiated by each individual antenna combine and superpose, adding together (interfering constructively) to enhance the power radiated in desired directions, and cancelling (interfering destructively) to reduce the power radiated in other directions. Similarly, when used for receiving, the separate radio frequency currents from the individual antennas combine in the receiver with the correct phase relationship to enhance signals received from the desired directions and cancel signals from undesired directions.
This presentation gives an insight about various antennas that were in use in cell phones and are being used in today's smartphones. Images of various phone models showing antenna used are included to give pictorial view of internal antennas
An Antenna is a transducer, which converts electrical power into electromagnetic waves and vice versa.
An Antenna can be used either as a transmitting antenna or a receiving antenna.
A transmitting antenna is one, which converts electrical signals into electromagnetic waves and radiates them.
A receiving antenna is one, which converts electromagnetic waves from the received beam into electrical signals.
In two-way communication, the same antenna can be used for both transmission and reception.
Basic Parameters
Frequency
Wavelength
Impedance matching
VSWR & reflected power
Bandwidth
Percentage bandwidth
Radiation intensity.
This thesis focuses on mobile phones antenna design with brief description about the historical development, basic parameters and the types of antennas which are used in mobile phones. Mobile phones antenna design section consists of two proposed PIFA antennas. The first design concerns a single band antenna with resonant frequency at GPS frequency (1.575GHz). The first model is designed with main consideration that is to have the lower possible PIFA single band dimensions with reasonable return loss (S11) and the efficiencies. Second design concerns in a wideband PIFA antenna which cover the range from 1800MHz to 2600MHz. This range covers certain important bands: GSM (1800MHz & 1900MHz), UMTS (2100MHz), Bluetooth & Wi-Fi (2.4GHz) and LTE system (2.3GHz, 2.5GHz, and 2.6GHz). The wideband PIFA design is achieved by using slotted ground plane technique. The simulations for both models are performed in COMSOL Multiphysics.
The last two parts of the thesis present the problems of mobile phones antenna. Starting with Specific absorption rate (SAR) problem, efficiency of Mobile phones antenna, and hand-held environment.
A dipole antenna is the simplest antenna but its radiation characteristics are very good. The main drawback of a dipole antenna is very narrow bandwidth. The analysis of a dipole antenna can be performed with integration of Hertzian dipoles.
Microstrip antenna (also known as a printed antenna) usually means an antenna fabricated using microstrip techniques on a printed circuit board (PCB). They are mostly used at microwave frequencies.
An antenna array (or array antenna) is a set of multiple connected antennas which work together as a single antenna, to transmit or receive radio waves. The individual antenna elements are connected to a single receiver or transmitter by feedlines that feed the power to the elements in a specific phase relationship. The radio waves radiated by each individual antenna combine and superpose, adding together (interfering constructively) to enhance the power radiated in desired directions, and cancelling (interfering destructively) to reduce the power radiated in other directions. Similarly, when used for receiving, the separate radio frequency currents from the individual antennas combine in the receiver with the correct phase relationship to enhance signals received from the desired directions and cancel signals from undesired directions.
This presentation gives an insight about various antennas that were in use in cell phones and are being used in today's smartphones. Images of various phone models showing antenna used are included to give pictorial view of internal antennas
An Antenna is a transducer, which converts electrical power into electromagnetic waves and vice versa.
An Antenna can be used either as a transmitting antenna or a receiving antenna.
A transmitting antenna is one, which converts electrical signals into electromagnetic waves and radiates them.
A receiving antenna is one, which converts electromagnetic waves from the received beam into electrical signals.
In two-way communication, the same antenna can be used for both transmission and reception.
Basic Parameters
Frequency
Wavelength
Impedance matching
VSWR & reflected power
Bandwidth
Percentage bandwidth
Radiation intensity.
This thesis focuses on mobile phones antenna design with brief description about the historical development, basic parameters and the types of antennas which are used in mobile phones. Mobile phones antenna design section consists of two proposed PIFA antennas. The first design concerns a single band antenna with resonant frequency at GPS frequency (1.575GHz). The first model is designed with main consideration that is to have the lower possible PIFA single band dimensions with reasonable return loss (S11) and the efficiencies. Second design concerns in a wideband PIFA antenna which cover the range from 1800MHz to 2600MHz. This range covers certain important bands: GSM (1800MHz & 1900MHz), UMTS (2100MHz), Bluetooth & Wi-Fi (2.4GHz) and LTE system (2.3GHz, 2.5GHz, and 2.6GHz). The wideband PIFA design is achieved by using slotted ground plane technique. The simulations for both models are performed in COMSOL Multiphysics.
The last two parts of the thesis present the problems of mobile phones antenna. Starting with Specific absorption rate (SAR) problem, efficiency of Mobile phones antenna, and hand-held environment.
A dipole antenna is the simplest antenna but its radiation characteristics are very good. The main drawback of a dipole antenna is very narrow bandwidth. The analysis of a dipole antenna can be performed with integration of Hertzian dipoles.
Microstrip antenna (also known as a printed antenna) usually means an antenna fabricated using microstrip techniques on a printed circuit board (PCB). They are mostly used at microwave frequencies.
MicroStrip Antenna
Introduction .
Micro-Strip Antennas Types .
Micro-Strip Antennas Shapes .
Types of Substrates (Dielectric Media) .
Comparison of various types of flat profile printed antennas .
Advantages & DisAdvantages of MSAs .
Applications of MSAs .
Radiation patterns of MSAs .
How to Optimizing the Substrate Properties for Increased Bandwidth ?
Comparing the different feed techniques .
Design of c slotted microstrip antenna using artificial neural network modeleSAT Journals
Abstract In this paper, neural network model has been used to estimation of resonance frequency of a coaxial feed C-slotted Microstrip Antenna. The Multi-Layer Perceptron Feed forward back Propagation (MLPFFBP) and Radial basis function Artificial Neural Network (RBFANN) have been used to implement the neural network model. A relative performance analysis of the proposed neural network for different training algorithms. Number of neurons and number of hidden layer is also carried out for estimating the resonance frequency. The method of moment (MOM) based IE3D software was used to generate data dictionary for training and validation set of ANN. The results obtain using ANN are compared with simulation feeding and found quite satisfactory and also it is concluded that RBFANN network is more accurate and fast compared to MLPFFBP network algorithm. Index Terms: Artificial Neural Network, C slot, Microstrip Antenna, Multilayer Feed Forward Networks, Radial basis function Artificial Neural Network, Resonance frequency.
Multilayered low pass microstrip filter using csrreSAT Journals
Abstract Multi-tracking system is a real time tracking platform which uses integration of technologies such as GPS and GSM. The platform supports multiple tracking devices for variety of applications such as live vehicle tracking, personal tracking and also assets tracking. The GPS device installed in the vehicle continuously moves with the vehicle and will calculate the co-ordinates with other related information at each position and then transmit this information via GSM to the tracking server, thus storing it in the database; which further can be viewed on electronic map, i.e., Google Map via Internet providing up-to-date information. This proposed system also supports for real time control like, if owner sends an SMS, it automatically turns of the ignition of vehicle or other different purposes. The overall system will be implemented in Micro-soft .NET technology in which C#.Net will be used for system components & for web based ASP.Net will be used. Keywords: GPS, GSM, SMS, Socket Listener, Tracking server.
These are the steps to be followed to use the Optimetrics feature in HFSS. This feature lets a user to optimize his/her design and its parameters by employing several techniques.
The development of a system simulation platform for Adaptive Cruise Control (ACC) radar working at 77 GHz is presented. The simulation platform allows us to test different radar architectures, modulation formats and detection algorithms as well as to simulate different scenarios, which improves the decision-making before and during the hardware development.
ATI's Radar Systems Analysis & Design using MATLAB Technical Training Short C...Jim Jenkins
This course provides a comprehensive description of radar systems analyses and design. A design case study is introduced and as the material coverage progresses throughout the course, and new theory is presented, requirements for this design case study are changed and / or updated, and the design level of complexity is also increased. This design process is supported with a comprehensive set of MATLAB-7 code developed for this purpose. By the end, a comprehensive design case study is accomplished. This will serve as a valuable tool to radar engineers in helping them understand radar systems design process. Each student will receive the instructor’s textbook MATLAB Simulations for Radar Systems Design as well as course notes.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A CPW-fed Rectangular Patch Antenna for WLAN/WiMAX ApplicationsIDES Editor
This paper presents a CPW fed Rectangular
shaped patch antenna for the frequency 3.42GHz which
falls in WiMAX and 5.25GHz for WLAN applications.
The measured -10dB impedance bandwidth is about
650MHz (2.98GHz-3.63GHz) for WiMAX and 833MHz
(4.95GHz-5.78GHz) for WLAN applications. The effect of
slot width, rectangular patch height, and substrate
dielectric constant have been evaluated. The results of
antenna are simulated by using Zeeland’s MOM based
IE3D tool. Two dimensional radiation patterns with
elevation and azimuth angles, VSWR<2, Return loss of
-24dB and -18dB for WiMAX and WLAN applications,
antenna efficiency about 90%, gain above 3.5dB are
obtained. The compact aperture area of the antenna is
46.2 X 41.66 mm2.
A new design of a microstrip rectenna at 5.8 GHz for wireless power transmiss...IJECEIAES
Due to the ever-increasing power demand, the need of electricity and eco-friendly power in every nook and corner of the world, many reaserch topics have been devoted to deal with this problematic. This paper is taking part of the proposed solutions with the presentation of a novel 5.8 GHz rectenna system for wireless power transmission applications. In one hand, a miniaturized 5.8 GHz circular polarized patch antenna has been designed and simulated by using the Advanced Design System (ADS). In the other hand, a rectifier structure has been investigated and optimized by the use of the Harmonic Balance method available in ADS. The circuit is based on 5 HSMS2820 Schottky diodes implemented in a voltage multiplier topology and a load resistance of 1 KOhm. Both of the structures have been validated by simulation and experimental results and good agreement has been concluded.
A wearable UHF RFID tag antenna-based metamaterial for biomedical applicationsjournalBEEI
The development of miniature antennas for bio-medical applications has attracted the attention of many researchers in the last years. In this letter, we provide a miniature antenna for the RFID tag for identifying patients in African and European hospitals. The proposed antenna is designed on a flexible silicon substrate with a relative dielectric constant of 11.9 and a thickness of 1.6mm. An in-depth study of the proposed wearable antenna was made in free space and on human tissue. The achieved results showed good performance in terms of miniaturization, bandwidth, impedance matching and, reading distance. The presented tag antenna is designed and simulated by using CST-MWS solver and the results were validated by HFSS and both results are in good agreement.
A Low Profile CombinedArray Antenna for Wireless Applicationdbpublications
In wireless communication, micro strip antenna is used for various applications such as Wireless local area network, Bluetooth, Cordless Telephones and ISM band. Here a low profile microstrip antenna is to be designed. The antenna is constructed with FR4 substrate having relative permittivity of 4.4 and thickness of 1.6 mm. The unique (symmetric) array design enhances the narrow bandwidth and gain. The design results in extended WLANin X-band applications. Using High Frequency Structure Simulator (HFSS) software package, the antenna is simulated and dimensions are adjusted to achieve the desired resonant frequencies for desired operation.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
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Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
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PHP Frameworks: I want to break free (IPC Berlin 2024)
Artificial intelligence in the design of microstrip antenna
1. Artificial Intelligence in the
Design of Microstrip
Antenna
By:
Raj Kumar Thenua
Vandana V. Thakare
Department of Electronics & Instrumentation Engineering
AEC, Agra, UP
2. Outline
Introduction
Methodology
Design of a microstrip line feed rectangular Microstrip Antenna using
IE3D EM Simulator
Analysis of a microstrip line feed rectangular Microstrip Antenna
using ANN
Application
Conclusion
Future scope of the work
Results
References
2 8/9/2012
3. Introduction
Accurate RF/Microwave design is crucial for the current
upsurge in VLSI, telecommunication and wireless technologies
Design at microwave frequencies is significantly different from
low-frequency and digital designs
Substantial development in RF/microwave CAD techniques
have been made during the last decade
Further advances in CAD are needed to address new design
challenges
Fast and accurate models are key to efficient CAD
Neural network based modeling and design could significantly
impact high-frequency CAD
3 8/9/2012
4. A Illustration Example
MICRO STRIP PATCH ANTENNA
radiating metallic patch on a ground substrate
patch can take different configurations but rectangular
and circular patches are the most popular one because of
ease of analysis and fabrication and their attractive
radiation characteristics
4 8/9/2012
6. Justification for Present Work
Antenna design is a very complex problem .
Spacecrafts,aircrafts,missiles and satellite
applications require antenna in small ,size
,weight,cost and easy to install.
Mobile ,radio and other wireless communications
also demands such specific antennas.
To fulfill such requirements Microstrip patch
antennas are used.
6 8/9/2012
7. Methodology
Development of an ANN model in Mat Lab Neural
Network Tool Box for the calculation of patch
dimensions for Microstrip Antenna .
The data for training the network is generated
using IE3D a Electro Magnetic Simulator.
As an example a microstrip line feed rectangular
Microstrip patch Antenna is being considered
and designed using simulator for a particular
resonating frequency i.e. 4.9 GHz.
Validation of ANN model
7 8/9/2012
8. IE3D Electro Magnetic Simulator
• Computer Aided Simulation
Integrated Electromagnetic three Dimensional
(IE3D) Software
Developed by Zeland Inc., United States
Design Dimensions can be milli, micro and so on.
Simulation Time – Few Minutes
Output Result can be obtained in the form of patch
dimensions , VSWR, Return loss, Gain Directivity
,Radiation efficiency,etc.
8 8/9/2012
9. Design parameters
designed in IE3D Simulator
With dielectric constant Єr = 4.7
Substrate thickness h = 1.588mm
Length L= 6.6 mm
Width W = 8.8 mm
Length of the feed l = 2 mm
Width of the feed w = 0.5mm
Resonating frequency fr = 4.9 GHz
9 8/9/2012
11. Relations to calculate different parameters of
rectangular patch antenna
The effective dielectric constant of the dielectric
material is given by
11 8/9/2012
12. Contd…
For an efficient radiator, a practical width that leads
to good radiation efficiencies is given by:
where vo is the free-space velocity of light.
12 8/9/2012
13. Contd..
The actual length of the patch:
where ∆L is the extension of the length due to the
fringing effects and is given by:
13 8/9/2012
16. IE3D Electromagnetic Simulator
to Generate Simulated Data
efficiency
h
gain
W
IE3D Input impedance
L
SOFT
Feed WARE VSWR
dimensions
Єr Return loss
frequency band
Figure 4.0
16 8/9/2012
18. Neural Network Model
The ANN model is a system with input vector s’
representing the circuit design parameters:
height of substrate= h
dielectric constant = Єr
cut off frequencies F1 and F2
And the output vector r’ representing the Patch
dimensions.
Length of the Patch = L
Width of the Patch = W
18 8/9/2012
19. Neural Network Architecture
Three layer network structure has been considered
Input layer will have four neurons to accept input
parameters h ,Єr, F1 and F2.
Output layer will have two neurons to output patch
dimensions.
The hidden layer will have number of neurons
depending upon design accuracy.
The radial basis function network is considered for
the network architecture.
The network will be trained using radial basis
function
19 8/9/2012
20. RBF Network
Feed forward neural networks with a single hidden layer
that use radial basis activation functions for hidden
neurons are called radial basis function networks.
RBF networks are applied for various microwave
modeling purposes.
RBF can approximate any regular function.
Trains faster than any multi-layer perceptron.
It has just two layers of weights.
Input is non-linear and output is linear.
No saturation while generating outputs
20 8/9/2012
21. Architecture of RBF Network
x1
y1
x2
y2
x3
output layer
input layer
(linear weighted sum)
(fan-out)
hidden layer
(weights correspond to cluster centre,
output function usually Gaussian)
21 8/9/2012
22. RBF Functions
Gaussian Activation Function
1
j x exp X j j X j j 1...L
Output Layer: is a weighted sum of hidden inputs
L
k (x) jk . j (x)
j 1
X is a multi dimensional input vector with elements xi and j is
the vector determining the center of basis function j and
has elements ji.
22 8/9/2012
23. Contd..
The distance measured from the cluster centre is
usually the Euclidean distance.
n
rj ( xi wij ) 2
i 1
23 8/9/2012
24. MAT Lab Tool Box
In order to develop the ANN model MAT LAB
neural network tool box has been used.
24 8/9/2012
25. Network Training
Two kinds of training algorithms
- Supervised and Unsupervised
- RBF networks are used mainly in supervised
applications
- In this case, both dataset and its output is known.
- The model is trained with the set of 200 samples
Clustering algorithms (k-mean)
The centers of radial basis functions are initialized
randomly.
For a given data sample Xi the algorithm adapts its
closest center
25 8/9/2012
26. Network Testing
The performance of the network is tested by a
second set of a sample vectors pairs which are not
included in training data set but must be in the
specified given range.
If the unknown sample pairs are modeled correctly
the network is likely to represent a valid model.
The model is tested for around 26 values and found
satisfactorily.
26 8/9/2012
27. Application
After training and testing the model is ready to be
used as a simulator for the calculation of patch
dimensions for the Microstrip antenna.
The model can be reused in the design process
many times without the cost of EM Simulations.
The network is capable of predicting the output for
any given input in the trained region inexpensively.
27 8/9/2012
28. Results
S. No. F1 GHz F2 GHz W mm L mm W mm L mm
(IE3D) (IE3D) (RBF) (RBF)
1 4.93 5.03 17.8 13.35 17.84 13.33
2 4.86 4.95 17.8 13.55 17.83 13.53
3 4.82 4.91 17.8 13.65 17.84 13.66
4 4.8 4.89 17.8 13.85 17.83 13.84
5 4.77 4.85 17.8 14.05 17.84 14.02
6 4.73 4.81 17.8 14.15 17.83 14.17
7 4.71 4.79 17.8 14.25 17.82 14.26
8 4.69 4.76 17.8 14.35 17.83 14.36
28 8/9/2012
29. W mm L mm W mm L mm
S. No. F1 GHz F2 GHz
(IE3D) (IE3D) (RBF) (RBF)
9 4.66 4.73 17.8 14.45 17.84 14.46
10 4.78 4.87 18.3 13.85 18.29 13.86
11 4.65 4.73 18.3 14.35 18.31 14.37
12 4.61 4.7 18.8 14.35 18.82 14.36
13 4.49 4.55 18.8 14.85 18.83 14.84
14 4.47 4.55 19.3 14.85 19.32 14.83
15 4.37 4.41 19.3 15.35 19.31 15.33
16 4.35 4.41 19.8 15.35 19.82 15.37
29 8/9/2012
30. W mm L mm W mm L mm
S. No. F1 GHz F2 GHz
(IE3D) (IE3D) (RBF) (RBF)
17 4.31 4.37 20.3 15.85 20.29 15.84
18 4.29 4.35 20.3 16.35 20.31 16.36
19 4.27 4.33 20.8 16.35 20.84 16.36
20 4.26 4.33 20.8 16.85 20.82 16.86
21 4.21 4.27 21.3 16.85 21.33 16.84
22 4.16 4.21 21.3 17.35 21.29 17.36
23 4.14 4.19 21.8 17.35 21.83 17.37
24 4.02 4.04 21.8 17.85 21.83 17.86
25 3.99 4.01 22.3 17.85 22.33 17.83
26 3.92 3.93 22.3 18.35 22.31 18.36
30 8/9/2012
31. Conclusion
The neural network developed in this work models
the patch dimensions calculator for microstrip line
feed rectangular Microstrip patch antenna.
The radial basis function network is giving the best
approximation to the target values
The values obtained from ANN are very close to
simulation readings .
The error between output of ANN and IE3D is very
very small.
The developed model for resonant structure
Microstrip Antenna validate the modeling approach.
31 8/9/2012
32. Future Scope
Working with the same concept and design
analysis ,different microwave and RF devices
could be designed .
Different analysis and synthesis ANN model
could be developed for other performance
parameters of the microwave circuits like input
impedance ,directivity ,gain, VSWR, return
loss etc.
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33. References
[1] Q. J. Zhang, K. C. Gupta, Neural Networks for RF and Microwave Design,
Artech House Publishers, 2000.
[2] R. K. Mishra, Member, IEEE, and A. Patnaik , ANN Techniques in
Microwave technology .
[3] A. H. Zaabab, Q.J. Zhang, M. Nakhla, ”Analysis and Optimization of
Microwave Circuits & Devices Using Neural Network Models”’ IEEE MTT-
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[4] C.A. Balanis, Antenna Theory, John Wiley & Sons, Inc., 1997.
[5] D.M. Pozar, Microstrip Antenna , Proc. IEEE, Vol. 80, pp.79-81,
[6] F. Wang, V.K. DevabhaktunI, and Q.J. Zhang,” A
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neural models for microwave design”,
IEEE Intl. Microwave Symp. Digest, pp. 1767-1770, Baltimore, MD, 1998.
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34. Contd..
[7] F. Peik, G. Coutts, R.R. Mansour ,COM DEV, Cambridge, ON, Canada,
“Application of neural networks in microwave circuit modelling” , Electrical
and computer Engineering,1998,IEEE Canadian Conference , vol-2 ,24-28 May
1998,pages:928-931
[8] S. Devi , D.C. Panda, S.S. Pattnaik, “A novel method of using Artificial neural
networks to calculate input impedance of circular microstrip antenna”,
Antennas and Propagation Society International Symposium, Vol. 3, pp. 462 – 465,
16-21 June 2002.
[9] R.K. Mishra, A. Patnaik, “Neural network-based CAD model for the design of
square-patch antennas”, Antennas and Propagation, IEEE Transactions, Vol. 46,
No. 12, pp. 1890 – 1891, December 1998.
[10] A. Patnaik, R.K. Mishra, G.K. Patra, S.K. Dash, ”An artificial Neural network
model for effective dielectric constant of microstrip line,”
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[11] Simon Haykin, Neural Networks second edition pHI
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