Google Earth Enterprise Documentation Home | Fusion tutorial
Setting Up the Tutorial
In Google Earth Enterprise Open Source, the fusion tutorial data is not included in the installation packages. Refer to Configure tutorial workspace for download and installation instructions.
In older versions, the Google Earth Enterprise Fusion tutorial data is provided on the Google Earth Enterprise Fusion installation DVD and should have been installed in /opt/google/share/tutorials/fusion/
on your workstation or network. In addition, your system administrator should have configured a tutorial environment for you to work on the tutorial lessons, keeping your practice data separate from your live production data.
If Google Earth Enterprise Fusion or the tutorial files are not installed or you encounter an error message that tells you that a tutorial source file is not readable or you cannot save a resource, contact your system administrator or refer to Configure tutorial workspace to install the files and configure the tutorial environment before saving any practice data.
As you follow the steps in this tutorial, you'll begin to learn how to create 2D and 3D databases, as well as becoming familiar with the Google Earth Enterprise Fusion user interface.
- Select the Tutorial Asset Root
- Launch Google Earth Enterprise Fusion
- Build the Asset Navigation Tree
- Define Source Providers
- Define Resource Paths
Select the Tutorial Asset Root
There are two occasions when you must select a different asset root:
- When multiple users share a single workstation, you must select your own tutorial asset root.
- When you switch from the tutorial data to real production data, you must select the appropriate asset root.
To configure asset roots for multiple users, see Configure tutorial workspace.
This section explains how to select a different asset root.
Caution: Even though your source volumes and asset roots are separate for each user or for the tutorial and production data, there is only one publish root on each virtual server for Earth databases and one for Map databases.
When two users are sharing a single workstation, both users are publishing to the same publish root. When one user publishes a database on that workstation, it overwrites any database that might have been published previously by another user on that same workstation. Likewise, if you are switching back and forth between tutorial and production data on the same workstation, it is possible to overwrite a production database with a tutorial database and vice versa. Of course, you can republish the desired database to make it available to Google Earth EC again.
To select the tutorial asset root:
- On the command line, log in as root.
- Stop the system manager by entering:
/etc/init.d/gefusion stop
- Enter:
where username is the name you or your system administrator used when configuring the tutorial asset root. (If you do not know the path of your tutorial asset root, contact your system administrator.)geselectassetroot --assetroot /username/assets
- When you return to the prompt, log out as root.
- Start the system manager by entering:
/etc/init.d/gefusion start
Launch Google Earth Enterprise Fusion
To launch Google Earth Enterprise Fusion:
- Open a Linux terminal window.
- Enter
fusion
(orfusion &
to return to the Linux prompt).
When the application starts, the Google Earth Enterprise Fusion graphical user interface (GUI) appears.
Build the Asset Navigation Tree
The components of a Google Earth Enterprise Fusion database are called assets. The main location where you store all of your Google Earth Enterprise Fusion assets is called the asset root. The asset root is located in the main Google Earth Enterprise Fusion volume--in most cases, /gevol/assets
. For the tutorial lessons, this guide refers to the tutorial asset root to differentiate it from the asset root that contains your live data.
In this exercise, you add subfolders to the tutorial asset root, so you can store your assets in an organized way.
Caution: Once you create subfolders in your asset root, you cannot delete them. This makes it especially important for you to plan out and organize your subfolders before you or anyone else begins working with real data in Google Earth Enterprise Fusion.
To add a subfolder:
- Open the Asset Manager by selecting Tools > Asset Manager.
The Asset Manager appears:
The asset navigation tree appears on the left side of the Asset Manager, andASSET_ROOT
is the only folder on the tree. - Right-click ASSET_ROOT, and select New Subfolder from the context menu.
The New Subfolder dialog appears. - Enter Resources as the name of the new subfolder, and click OK.
The new subfolder appears in the asset navigation tree. - In the same way, add three more subfolders:
- MapLayers
- Projects
- Databases
- For the Projects and Resources folders you just created, right-click to create the following subfolders:
- Vector
- Imagery
- Terrain
- Close the Asset Manager by clicking the close box (X) in the top right corner.
Define Source Providers
The Provider Manager allows you to create a list of organizations that provide the source data you use in Google Earth. For each provider, you specify a unique lookup key (any unique abbreviation you choose) and copyright information. When you create a resource, a data provider is associated with it, so that when the resource is displayed in Google Earth EC, the appropriate copyright information is also displayed.
To create a source provider:
- Open the Provider Manager by selecting Tools > Provider Manager.
- Click the page icon:
The Edit Provider dialog appears.
- Enter the following information:
Name: USGS Imagery
Key: USGS-I
Copyright Text: Imagery © 2005 USGS
To enter the copyright symbol, open a text editor, press CTRL-SHIFT-U, then enter the Unicode 00A9. Copy and paste the displayed symbol into the dialog. - Click OK.
- Repeat steps 2 through 4 to add the following source providers:
Name Key Copyright Text i3 i3 Imagery © 2005 i3 USGS Maps USGS-M Roads © 2005 USGS GNIS/US Census Bureau GNIS Population © 2005 GNIS USGS POIs USGS-P POIs © 2004 USGS USGS Terrain USGS-T Terrain © USGS 2003 NASA Imagery NASA Imagery courtesy NASA’s Earth Observatory
The new providers appear in the Provider Manager dialog in the order in which you added them:
- Click OK to close the Provider Manager.
Define Resource Paths
The Asset Manager Resource Editor Default Source Paths screen allows you to specify default paths for Vector, Imagery, and Terrain resource types.
To define resource paths:
- Open the Asset Manager Resource Editor Default Source Paths by selecting Edit > Preferences > Asset Manager.
- Click the first folder icon on the right-hand side for Vector.
The Select Folder dialog appears.
- Navigate to /opt/google/share/tutorials/fusion.
- Select Vector, then click OK.
- Repeat steps 2 through 4 for Imagery and Terrain.
- Click OK to close the Asset Manager Resource Editor Default Source Paths, and go on to the next lesson.


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{% if groups|length > 0 %}{% if is_admin %}{{ gettext("See all uses.") }} {% endif %}{{ gettext("You're in charge of the following groups:") }}
{% from "_widgets.html" import show_groups %} {{ show_groups(tree_groups, groups) }} {% else %}{{ gettext("You don't have permission of teacher on any group (or there is no group in the system)") }}


This text is displayed if your browser does not support HTML5 Canvas.
Your browser does not have JavaScript enabled, please enable it.


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--- layout: page title: Contact description: "email me" meta-title: "Contact" meta-description: "contact details" redirect_from: - /contact.php/ ---
steven.powell@bestcoders.co.uk


Judges 21:22
And it shall be, when their fathers or their brethren come unto us to complain, that we will say unto them, Be favorable unto them for our sakes: because we reserved not to each man his wife in the war: for ye did not give unto them at this time, that ye should be guilty.


| Cairo.onSave {Cairo} | R Documentation |
Cairo.onSave
set the onSave
callback which allows R code
to be run when Cairo finalizes a page (either due to a new page being
created or by the device being closed). The callback expects
function(device, page)
where device
will be the device
number and page
is the currently finished page number (starting
at 1).
Cairo.onSave(device = dev.cur(), onSave)
device |
device number or |
onSave |
function that will replace the current callback or
|
The old callback being replaced or NULL
if there was none.
The function onSave
will be evaluated in the global environment
and no error checking is done, so you must make sure to catch errors,
otherwise the behavior is undefined (and may included crashing R or
other bad things).
Simon Urbanek
if (require(png, quietly=TRUE)) { dev <- Cairo(800, 600, type='raster') Cairo.onSave(dev, function(dev, page) .GlobalEnv$png <- writePNG(Cairo.capture(dev)) ) plot(1:10, col=2) dev.off() str(png) }


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--- layout: default officecolor: "#4C9509" office: Information Innovation Office (I2O) ---
DARPA's Programming Computation on Encrypted Data (PROCEED) program is a research effort that seeks to develop methods that allow computing with encrypted data without first decrypting that data, making it possible to compute more securely in untrusted environments.
Program Complete
The content below has been generated by organizations that are partially funded by DARPA; the views and conclusions contained therein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of DARPA or the U.S. Government.
Report a problem<EMAIL_ADDRESS>Last updated: November 13, 2015
| Team | Title | Link |
|---|---|---|
| MIT | Functional Encryption for Inner Product Predicates from Learning with Errors | http://eprint.iacr.org/2011/410.pdf |
| University of Virginia | Computing on Authenticated Data | http://eprint.iacr.org/2011/096.pdf |
| Stanford University | A Domain-Specific Language for Computing on Encrypted Data | http://eprint.iacr.org/2011/561.pdf |
| UCLA | Deterministic and Energy-Optimal Wireless Synchronization | http://arxiv.org/pdf/1010.1112 |
| University of Texas | Standard Security Does Not Imply Security Against Selective-Opening | http://eprint.iacr.org/2011/581.pdf |
| University of Texas | Identity-Based (Lossy) Trapdoor Functions and Applications | http://eprint.iacr.org/2011/479.pdf |
| MIT | Program Obfuscation with Leaky Hardware | https://eprint.iacr.org/2011/660.pdf |
| Stanford University | Random Oracles in a Quantum World | http://eprint.iacr.org/2010/428.pdf |
| Stanford University | Homomorphic Signatures for Polynomial Functions | http://theory.stanford.edu/~dfreeman/papers/homsigs.pdf |
| Stanford University | Targeted Malleability: Homomorphic Encryption for Restricted Computations | http://eprint.iacr.org/2011/311.pdf |
| MIT | On Sum Coloring and Sum Multi-coloring for Restricted Families of Graphs | http://www.cs.toronto.edu/~bor/2420f10/stacs.pdf |
| MIT | Multiparty Computation Secure Against Continual Memory Leakage | http://www.cs.technion.ac.il/~elette/LR-MPC-STOC12.pdf |
| MIT | Leakage-Resilient Coin Tossing | https://eprint.iacr.org/2011/291 |
| MIT | Fully Homomorphic Encryption without Bootstrapping | https://eprint.iacr.org/2011/277.pdf |
| Stanford University | Fully Homomorphic Encryption without Modulus Switching from Classical GapSVP | http://eprint.iacr.org/2012/078.pdf |
| UCLA | Position-Based Quantum Cryptography: Impossibility and Constructions | https://eprint.iacr.org/2010/275.pdf |
| UCLA | Simultaneously Resettable Arguments of Knowledge | http://link.springer.com/chapter/10.1007%2F978-3-642-28914-9_30#page-1 |
| University of Virginia | The Randomness Complexity of Parallel Repetition | http://www.cs.cornell.edu/~rafael/papers/rand-para.pdf |
| University of Virginia | The Knowledge Tightness of Parallel Zero-Knowledge | http://www.cs.cornell.edu/~chung/download/parallelZKKnowledge.pdf |
| BBN | SIPHER: Scalable Implementation of Primitives for Homomorphic EncRyption - FPGA implementation using Simulink | http://q.hscott.net/reads/HPEC11_Cousins.pdf |
| BBN | An Update on Scalable Implementation of Primitives for Homomorphic EncRyption - FPGA implementation using Simulink | http://www.bibsonomy.org/bibtex/2f384310febd0bacdd84a0920d68d3f2b/dblp |
| UCLA | Resettable Cryptography in Constant Rounds - The Case of Zero Knowledge | https://eprint.iacr.org/2011/408.pdf |
| UCLA | Leakage-Resilient Zero Knowledge | http://www.cs.ucla.edu/~sanjamg/Sanjam%20Garg_files/GJS11.pdf |
| UCLA | Resettable Statistical Zero Knowledge | http://eprint.iacr.org/2011/457.pdf |
| UCLA | Round Optimal Blind Signatures | http://eprint.iacr.org/2011/264.pdf |
| UCLA | Efficient and Explicit Coding for Interactive Communication | http://people.csail.mit.edu/moitra/docs/treecode.pdf |
| IBM | Fully Homomorphic Encryption without Squashing Using Depth-3 Arithmetic Circuits | https://eprint.iacr.org/2011/279 |
| IBM | FHE with Polylog Overhead | http://eprint.iacr.org/2011/566 |
| IBM | Better Bootstrapping in Fully Homomorphic Encryption | http://eprint.iacr.org/2011/680.pdf |
| IBM | Ring Switching in BGV-Style Homomorphic Encryption | http://link.springer.com/chapter/10.1007%2F978-3-642-32928-9_2#page-1 |
| IBM | Homomorphic Evaluation of the AES circuit | http://eprint.iacr.org/2012/099.pdf |
| MIT | Bounded-Collusion IBE from Key Homomorphism | http://web.mit.edu/dwilson/www/papers/glw.pdf |
| University of Virginia | Outsourcing the Decryption of ABE Ciphertexts | http://static.usenix.org/event/sec11/tech/full_papers/Green.pdf |
| UCLA | Lossy Encryption: Constructions from General Assumptions and Efficient Selective Opening Chosen Ciphertext Security | https://eprint.iacr.org/2009/088 |
| UCLA | Public Key Locally Decodable Codes with Short Keys | http://eccc.hpi-web.de/report/2011/118/download |
| University of Virginia | Detecting Dangerous Queries: A New Approach for Chosen Ciphertext Security | http://eprint.iacr.org/2012/006.pdf |
| UCLA | Constant-Rate Oblivious Transfer from Noisy Channels | http://www.iacr.org/conferences/crypto2011/abstracts/664.htm |
| UCLA | Efficient Non-interactive Secure Computation | http://www.cs.illinois.edu/~mmp/pub/nisc-proceedings.pdf |
| UCLA | Identifying Cheaters Without an Honest Majority | http://link.springer.com/chapter/10.1007%2F978-3-642-28914-9_2#page-1 |
| UCLA | Cryptography with Tamperable and Leaky Memory | http://research.microsoft.com/en-us/um/people/yael/publications/2011-Cryptography_with_Tamperable_and_Leaky_Memory.pdf |
| UCLA | On the (In)security of Hash-based Oblivious RAM and a New Balancing Scheme | http://www.researchgate.net/publication/220779318_On_the_%28in%29security_of_hash-based_oblivious_RAM_and_a_new_balancing_scheme |
| Stanford University | Trapdoors for Lattices: Simpler, Tighter, Faster, Smaller | https://eprint.iacr.org/2011/501 |
| Georgia Tech | Memory-Efficient Garbled Circuit Generation for Mobile Devices | http://ix.cs.uoregon.edu/~butler/pubs/fc11.pdf |
| BBN | Bi-Deniable Public-Key Encryption | http://www.cc.gatech.edu/~cpeikert/pubs/deniable.pdf |
| MIT | How to Delegate and Verify in Public: Verifiable Computation from Attribute-based Encryption | http://research.microsoft.com/apps/pubs/default.aspx?id=157921 |
| MIT | Computing Blindfolded: New Developments in Fully Homomorphic Encryption | http://www.cs.utoronto.ca/~vinodv/FHE-focs-survey.pdf |
| University of Texas | Dual Form Signatures: An Approach for Proving Security from Static Assumptions | http://eprint.iacr.org/2012/261.pdf |
| University of Texas | Functional Encryption for Regular Languages | http://eprint.iacr.org/2012/384.pdf |
| UCLA | Dynamic Credentials and Ciphertext Delegation for Attribute-Based Encryption | https://eprint.iacr.org/2012/437.pdf |
| University of Texas | New Proof Methods for Attribute-Based Encryption: Achieving Full Security through Selective Techniques | http://eprint.iacr.org/2012/326.pdf |
| UCI | Fully Homomorphic Encryption for Mathematicians | http://eprint.iacr.org/2013/250.pdf |
| UCLA | Approximating Large Frequency Moments with Pick-and-Drop Sampling | http://arxiv.org/abs/1212.0202 |
| UCLA | Generalizing the Layering Method of Indyk and Woodruff: Recursive Sketches for Frequency-Based Vectors on Streams | http://link.springer.com/chapter/10.1007%2F978-3-642-40328-6_5#page-1 |
| UCLA | On Linear-Size Pseudorandom Generators and Hardcore Functions | http://link.springer.com/chapter/10.1007%2F978-3-642-38768-5_17 |
| UCLA | Cryptography Using Captcha Puzzles | http://eprint.iacr.org/2012/689 |
| UCLA | Concurrent Zero Knowledge in the Bounded Player Model | http://eprint.iacr.org/2012/279.pdf |
| UCLA | Succinct Non-interactive Arguments via Linear Interactive Proofs | https://eprint.iacr.org/2012/718 |
| UCLA | Revisiting Lower and Upper Bounds for Selective Decommitments | http://eprint.iacr.org/2011/536 |
| UCLA | New Techniques for Noninteractive Zero-Knowledge | http://www0.cs.ucl.ac.uk/staff/J.Groth/NIZKJournal.pdf |
| UCLA | The Effectiveness of Lloyd-type Methods for the k-Means Problem | http://www.cs.ucla.edu/~rafail/PUBLIC/76.pdf |
| UCLA | Impossibility Results for Static Input Secure Computation | http://eprint.iacr.org/2012/433.pdf |
| UCLA | Near-Linear Unconditionally-Secure Multiparty Computation with a Dishonest Minority | http://eprint.iacr.org/2011/629.pdf |
| UCLA | Unconditionally-Secure Robust Secret Sharing with Compact Shares | http://www.cs.bris.ac.uk/eurocrypt2012/Program/Mon/Fehr.pdf |
| UCLA | Constructing Non-malleable Commitments: A Black-Box Approach | http://research.microsoft.com/en-us/um/people/vipul/bbnmcom.pdf |
| UCLA | Nearly Simultaneously Resettable Black-Box Zero Knowledge | http://link.springer.com/chapter/10.1007%2F978-3-642-31594-7_8#page-1 |
| UCLA | Edge Fault Tolerance on Sparse Networks | http://research.microsoft.com/en-us/um/people/nichandr/edgefaulttolerance.pdf |
| UCLA | Multiparty Proximity Testing with Dishonest Majority from Equality Testing | http://eprint.iacr.org/2012/378.pdf |
| Bristol | Multiparty computation from somewhat homomorphic encryption | https://eprint.iacr.org/2011/535 |
| Bristol | Implementing AES via an Actively/Covertly Secure Dishonest-Majority MPC Protocol | http://eprint.iacr.org/2012/262.pdf |
| Stanford University | Classical Hardness of Learning with Errors | http://arxiv.org/pdf/1306.0281 |
| BBN | A Toolkit for Ring-LWE Cryptography | http://www.di.ens.fr/~lyubash/papers/toolkit.pdf |
| BBN | How to Share a Lattice Trapdoor: Threshold Protocols for Signatures and (H)IBE | http://eprint.iacr.org/2013/419.pdf |
| University of Virginia | Zerocoin: Anonymous Distributed e-Cash from Bitcoin | http://spar.isi.jhu.edu/~mgreen/ZerocoinOakland.pdf |
| University of Virginia | Machine-Generated Algorithms, Proofs and Software for the Batch Verification of Digital Signature Schemes | http://eprint.iacr.org/2013/175 |
| University of Virginia | Vis-a-Vis Cryptography: Private and Trustworthy In-Person Certifications | https://www.usenix.org/system/files/conference/healthsec12/healthsec12-final25.pdf |
| Stanford University | An Equational Approach to Secure Multi-Party Computation | http://cseweb.ucsd.edu/~daniele/papers/ITCS13.pdf |
| Cybernetica | AES block cipher implementation and secure database join on the Sharemind secure multi-party computation framework | Not Yet Published |
| University of Virginia | Unprovable Security of Perfect NIZK and Non-interactive Non-malleable Commitments | http://www.cs.cornell.edu/~rafael/papers/limits2new.pdf |
| University of Virginia | On the Power of Many 1-Bit Provers | http://arxiv.org/abs/1301.2729 |
| University of Virginia | On The Power of Non-uniform Proofs of Security | http://www.cs.cornell.edu/~chung/download/nonuniform-camera.pdf |
| University of Virginia | UC from OT | Not Yet Published |
| University of Virginia | Blackbox Construction of Composable Protocols | http://www.cs.cornell.edu/~rafael/papers/bbsmc.pdf |
| University of Virginia | The Curious Case of Non-interactive Commitments | http://eprint.iacr.org/2012/523 |
| University of Virginia | Billion-gate Secure Two-party Computation | http://eprint.iacr.org/2012/179 |
| University of Virginia | Non-Black-Box Simulation from One-Way Functions And Applications to Resettable Security | http://eprint.iacr.org/2013/008.pdf |
| Stanford University | When Homomorphism Becomes a Liability | http://eprint.iacr.org/2012/225.pdf? |
| Stanford University | Quantum-secure Message Authentication Codes | http://eprint.iacr.org/2012/606.pdf |
| IBM | Dynamic Proofs of Retrievability via Oblivous RAM | http://eprint.iacr.org/2012/550 |
| UCLA | How to Catch L 2-Heavy-Hitters on Sliding Windows | http://arxiv.org/pdf/1012.3130 |
| UCLA | Optimal Coding for Streaming Authentication and Interactive Communication | http://eccc.hpi-web.de/report/2012/104/download/ |
| UCLA | Universally Composable Secure Computation with (Malicious) Physically Uncloneable Functions | http://eprint.iacr.org/2012/143 |
| UCLA | How Hard Is Counting Triangles in the Streaming Model? | http://arxiv.org/pdf/1304.1458 |
| UCLA | Local Correctability of Expander Codes | http://arxiv.org/abs/1304.8129 |
| UCLA | Robust Pseudorandom Generators | http://eprint.iacr.org/2013/671.pdf |
| BBN | Practical Bootstrapping with Polylogarithmic Overhead | Not Yet Published |
| Stanford University | Hardness of SIS and LWE with Small Parameters | https://eprint.iacr.org/2013/069.pdf |
| BBN | Accelerating Computations on Encrypted Data with an FPGA | http://www.mathworks.com/company/newsletters/articles/accelerating-computations-on-encrypted-data-with-an-fpga.html |
| Cybernetica | From Oblivious AES to Efficient and Secure Database Join in the Multiparty Setting | http://eprint.iacr.org/2013/203 |
| Yale University | Structural Cloud Audits that Protect Private Information | http://www.cs.yale.edu/homes/jf/ccsw-24-xiao.pdf |
| Georgia Tech | Secure Outsourced Garbled Circuit Evaluation for Mobile Devices | http://0b4af6cdc2f0c5998459-c0245c5c937c5dedcca3f1764ecc9b2f.r43.cf2.rackcdn.com/12310-sec13-paper_carter.pdf |
| University of Virginia | PCF: A Portable Circuit Format for Scalable Two-Party Secure Computation | http://0b4af6cdc2f0c5998459-c0245c5c937c5dedcca3f1764ecc9b2f.r43.cf2.rackcdn.com/12312-sec13-paper_kreuter.pdf |
| Georgia Tech | For Your Phone Only: Custom Protocols for Efficient Secure Function Evaluation on Mobile Devices | http://onlinelibrary.wiley.com/doi/10.1002/sec.851/abstract |
| UCLA | Predicate Encryption Supporting Disjunctions, Polynomial Equations, and Inner Products | https://eprint.iacr.org/2007/404.pdf |
| UCLA | Sequential Aggregate Signatures, Multisignatures, and Verifiably Encrypted Signatures Without Random Oracles | http://cseweb.ucsd.edu/~hovav/dist/agg-sig.pdf |
| UCLA | Zero Knowledge LTCs and Their Applications | http://link.springer.com/chapter/10.1007%2F978-3-642-40328-6_42#page-1 |
| UCLA | Homomorphic Encryption from Learning with Errors: Conceptually-Simpler, Asymptotically-Faster, Attribute-Based | http://eprint.iacr.org/2013/340.pdf |
| UCLA | Accuracy-Privacy Tradeoffs for Two-Party Differentially Private Protocols | http://research.microsoft.com/jump/192517 |
| MIT | Secure Computation Against Adaptive Auxiliary Information | http://research.microsoft.com/en-us/um/people/yael/publications/2013-secure-adaptive-aux.pdf |
| IBM | Attribute-Based Encryption for Circuits from Multilinear Maps | http://eprint.iacr.org/2013/128 |
| University of Virginia | Full Domain Hash from (Leveled) Multilinear Maps and Identity-Based Aggregate Signatures | http://eprint.iacr.org/2013/434 |
| IBM | Witness encryption and its applications | http://eprint.iacr.org/2013/258.pdf |
| Bristol | Practical Covertly Secure MPC for Dishonest Majority - or: Breaking the SPDZ Limits | http://eprint.iacr.org/2012/642.pdf |
| Bristol | Estimating Key Sizes For High Dimensional Lattice Based Systems | http://eprint.iacr.org/2013/630.pdf |
| Bristol | Between a Rock and a Hard Place: Interpolating Between MPC and FHE | http://eprint.iacr.org/2013/085.pdf |
| University of Virginia | GPU and CPU Parallelization of Honest-but-Curious Secure Two Party Computation | http://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=4&cad=rja&ved=0CD8QFjAD&url=http%3A%2F%2Fwww.cs.indiana.edu%2F~nhusted%2Fpresentations%2FACSAC2013-GPUGC.pptx&ei=AmDpUqeEGdTMsASXpoDADA&usg=AFQjCNGD5W9EBahO_T-fkYDn75AZ-t5Ctg&sig2=bq1w2wfg1HUfF6zdIW-Qzw&bvm=bv.60157871,d.cWc |
| University of Virginia | Using SMT Solvers to Automate Design Tasks for Encryption and Signature Schemes | http://dl.acm.org/citation.cfm?id=2516718 |
| University of Virginia | Fast Two-Party Secure Computation with Minimal Assumptions | http://eprint.iacr.org/2013/196.pdf |
| University of Virginia | Black-Box Proof of Knowledge of Plaintext and Multiparty Computation with Low Communication Overhead | http://link.springer.com/chapter/10.1007%2F978-3-642-36594-2_23#page-1 |
| University of Virginia | Knowledge-Preserving Interactive Coding | http://www.iis.sinica.edu.tw/~kmchung/download/KP-coding-main.pdf |
| University of Virginia | Constant-round Concurrent Zero-knowledge from P-Certificates | http://eprint.iacr.org/2012/563.pdf |
| University of Virginia | From Unprovability to Environmentally Friendly Protocols | http://www.cs.cornell.edu/~rafael/papers/EnvFriendly-proc.pdf |
| University of Virginia | Simultaneous Resettability From One-way Functions | http://www.iis.sinica.edu.tw/~kmchung/download/rsrZK.pdf |
| University of Texas | Constrained Pseudorandom Functions and Their Applications | http://eprint.iacr.org/2013/352.pdf |
| University of Texas | New Constructions and Proof Methods for Large Universe Attribute-Based Encryption | https://eprint.iacr.org/2012/583.pdf |
| University of Texas | Candidate Indistinguishability Obfuscation and Functional Encryption for All Circuits | http://eprint.iacr.org/2013/451.pdf |
| University of Texas | Encoding Functions with Constant Online Rate or How to Compress Garbled Circuits Keys | http://eprint.iacr.org/2012/693.pdf |
| MIT | Attribute-Based Encryption for Circuits | http://eprint.iacr.org/2013/337.pdf |
| MIT | Reusable Garbled Circuits and Succinct Functional Encryption | http://eprint.iacr.org/2012/733.pdf |
| MIT | Overcoming the Worst-Case Curse for Cryptographic Constructions | http://people.csail.mit.edu/nickolai/papers/goldwasser-we-eprint.pdf |
| MIT | Functional Encryption: New Perspectives and Lower Bounds | http://eprint.iacr.org/2012/468.pdf |


National Statistics
Statistics on the size and characteristics of the school workforce in state-funded schools.
PDF , 761KB , 32 pages
MS Excel Spreadsheet , 575KB
MS Excel Spreadsheet , 63.5KB
If you use assistive technology (such as a screen reader) and need a version of this document in a more accessible format, please email<EMAIL_ADDRESS>. Please tell us what format you need. It will help us if you say what assistive technology you use.
ZIP , 8.5MB
Plain text , 1.14KB
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MS Excel Spreadsheet , 252KB
If you use assistive technology (such as a screen reader) and need a version of this document in a more accessible format, please email<EMAIL_ADDRESS>. Please tell us what format you need. It will help us if you say what assistive technology you use.
This publication provides a single reference for all school workforce statistics based on staff working in publicly funded schools in England.
We have updated it to include tables referring to pupil to teacher ratios.
The release also includes information underlying the national tables at:
On 22 January 2015 we published additional tables with statistics on teachers who:
Published: 10 April 2014
Updated: 3 July 2015
From: Department for Education
Part of: Statistics: school workforce


Protocols
The following protocols are available globally.
-
Defines an object that is responsible for loading
MDLAsset
asset models. The AugmentKit render engine loads models as they are needed in the AR world. AModelProvider
implementation is responsible for loadingMDLAsset
‘s when requested and caching them as needed.AugmentKit provides
See moreAKModelProvider
as a default implementation of this protocol that caches allMDLAsset
objects in memory. Although this is the simplest implementation, you may need to provide your own implementation ofModelProvider
in order to provide a more economical caching strategy.Declaration
Swift
public protocol ModelProvider
-
An object that adheres to the
See moreAKWorldMonitor
protocol can receive updates when the world state changes.Declaration
Swift
public protocol AKWorldMonitor
-
An
AKAnchor
represents an object that can be rendered into theAKWorld
at an anchored (fixed) position. TheAKAnchor
protocol has two sub protocols,AKAugmentedAnchor
andAKRealAnchor
that represent augmented objects (objects that don’t exist in the real world) and real objects, objects that exist both in the augmented work and the real world.An
See moreAKAnchor
is aAKGeometricEntity
. The object itself may have animation but its position is essentially fixed in space. The object’s geometry is defiened by themodel
property. The objects position in the world is defined by the worldLocation property. The conforming object must be a class, i.e. must have reference symantics.Declaration
Swift
public protocol AKAnchor : AnyObject, AKGeometricEntity
-
Undocumented
See moreDeclaration
Swift
public protocol AKPath : AKGeometricEntityGroup
-
An AR anchor that represents a beginning or end of a path segment. Two or more of these can be used to create a path in the AR world. These anchors have an empty model so they cannot be rendered by themselves, They must be used on conjunction with an
See moreAKPath
object.Declaration
Swift
public protocol AKPathSegmentAnchor : AKAugmentedAnchor
-
And anchor representing a surface plane that has been detected in the real world. Usually these are provided by the AR engine, not created by hand.
See moreDeclaration
Swift
public protocol AKRealSurfaceAnchor : AKRealAnchor
-
Describes a animatable value. That is, a value that changes over time.
See moreDeclaration
Swift
public protocol AKAnimatable
-
Describes an
See moreAKAnimatable
that oscillates between aminValue
andmaxValue
over a timeperiod
Declaration
Swift
public protocol AKPulsingAnimatable : AKAnimatable
-
Describes an effect that can be applied to a model.
See moreAKEffect
s are evalueated and applied by the shader and apply to the model as a whole.AKEffect
s are animatableDeclaration
Swift
public protocol AKEffect : AKAnimatable
-
An
See moreAKEntity
defines an object that can be used and tracked by AugmentKit. Each instance of anAKEntity
is uniquely represented by it’sidentifier
and can be classified bytype
.Declaration
Swift
public protocol AKEntity
-
An
See moreAKGeometricEntity
represents a 3D geomentry existing in world space. It may be a rendered augmented object like a virtual sign or piece of furniture, or it may be a non-rendered reference geomentry that is used as meta information about the AR world like a 2D plane might represent a table top or a wall. The object’s geometry is defiened by themodel
property and may contain animation therefore the geomentry it does not always have to represent a rigid body. AKGeometricEntity does not contain any position information so it does not mean anything by itself in the context of the AKWorld. It does serve as a base protocol for more meaningful Types likeAKAnchor
andAKTracker
.Declaration
Swift
public protocol AKGeometricEntity : AKEntity
-
An
See moreAKGeometricEntityGroup
defines a group ofAKGeometricEntity
objects.Declaration
Swift
public protocol AKGeometricEntityGroup : AKEntity
-
An
See moreAKHeading
represents a angular rotation in 3d space. The rotation can be absolute (a rotation relative to the global x,y,z axis), or relative (a rotation relative to a local x, y, z axis). When theHeadingType
is set to.absolute
, the AugmentKit render engine uses the value ofoffsetRotation
to rotate the object to this orientation. When theHeadingType
is set to.relative
, the AugmentKit render engine combines the value ofoffsetRotation
with the parent objects rotation to rotate the object to it’s final orientation. TheupdateHeading(withPosition:)
method is called once per frame to give the object an opportunity to update itself before the final rotation calculations are made.Declaration
Swift
public protocol AKHeading
-
AKWorldLocation is a protocol that ties together a position in the AR world with a locaiton in the real world. When the
See moreARKit
session starts up, it crates an arbitrary coordinate system where the origin is where the device was located at the time of initialization. Every device and every AR session, therefore, has it’s own local coordinate system. In order to reason about how the coordinate system relates to actual locations in the real world, AugmentKit uses location services to map a point in theARKit
coordinate system to a latitude and longitude in the real world and stores this as aAKWorldLocation
instance. Once a reliableAKWorldLocation
is found, otherAKWorldLocation
objects can be derived by calculating their relative distance from the one reliable reference AKWorldLocation object.Declaration
Swift
public protocol AKWorldLocation
-
An
AKTarget
represents an object that can be rendered into theAKWorld
. It’s position is determined by a relative position (similar to anAKTracker
) and a direction vector. The vector extends from the relative position and where it intersects another object in theAKWorld
is where the target is rendered. A good example of this would be an augmented reality laser pointer. The red dot of the laser is where the object is rendered but where that dot is located in the AKWorld depends on where you hold the laser pointer (the position), the direction the laser pointer is pointed (the vector), and what’s in its path (an intersection with another object).An
AKTarget
is aAKGeometricEntity
.- Example:
An
AKTarget
might be the red dot of a laser pointer. Where the laser intersects a screen is be where the geometry of the dot is rendered. The location of the intersection is determined by the vector representing the direction the laser pointer is pointed as well as the postion of the laser pointer itself in the world.
Declaration
Swift
public protocol AKTarget : AnyObject, AKGeometricEntity
- Example:
An
-
Represents a tracker placed in the AR world. This tracker only exists in the AR world as opposed to a real tracker like a person or a car which exists in the physical world.
Declaration
Swift
public protocol AKAugmentedTracker : AKTracker
-
Represents a tracker in the AR world that is tied to an moving object in the real world. For example a person or a car.
Declaration
Swift
public protocol AKRealTracker : AKTracker
-
An
AKTracker
represents an object that can be rendered into theAKWorld
. It’s position is relative to another object and therefore tracks the other opject and is not fixed. TheAKTracker
protocol has two sub protocols,AKAugmentedTracker
andAKRealTracker
that represent augmented objects (objects that don’t exist in the real world) and real objects, objects that exist both in the augmented work and the real world.An
See moreAKTracker
is aAKGeometricEntity
. The object’s geometry is defiened by themodel
property. The object’s position is not fixed in world space but is relative.Declaration
Swift
public protocol AKTracker : AnyObject, AKGeometricEntity
-
Defines a managers for the users local store.
See moreDeclaration
Swift
public protocol LocalStoreManager
-
Describes ab object responsible for aquiring and monitoring the users location.
See moreDeclaration
Swift
public protocol LocationManager
-
Defines an objecgt that provides the
See moreCAMetalDrawable
as well as the currentMTLRenderPassDescriptor
and some other render properties.Declaration
Swift
public protocol RenderDestinationProvider
-
Describes an object that can be updated with the latest statistics and errors
See moreDeclaration
Swift
public protocol RenderMonitor
-
Describes an object that will be updated with status about the current render state
See moreDeclaration
Swift
public protocol RenderDelegate


This busy little fishng port lies in the eastern coast of the Pelopnnsos.
Position 37°24.8'N, 22°46'E.
Moor to the south mole taking care to avoid the underwater obstructions. The holding can be poor.
Last updated August 2009.


Spanish police find vast cannabis plantations hidden deep in forests
by Sam JonesSix people arrested and 16,000 marijuana plants seized during operation in Aragón region
Spanish police have arrested six people and seized 16,000 marijuana plants after an investigation into a spate of robberies led officers to two huge and sophisticated drug plantations hidden in remote pine forests.
The operation began seven months ago when the national police force learned that an Albanian gang had been stealing cigarette vending machines from bars and petrol stations in the north-eastern region of Aragón.
Officers discovered that the same gang was also growing marijuana on a massive scale and had set up camps to keep a constant eye on the crops.
The plantations, deep in the pine forests near Agüero and Murillo de Gállego in Aragón, could be reached only after a 30-minute drive in a 4x4 vehicle and a further 30 minutes on foot along difficult paths.
“The gang had cut down large areas of pine trees to maximise the amount of space for growing marijuana,” police said in a statement.
“They had also adapted gullies to store water, which was then fed to the plantations using pumps, hoses and electrical generators.”
Officers found the sites were guarded day and night.
“To do so, the gang had established camps that had everything needed for lengthy stays, including a large supply of non-perishable goods and even a stone oven,” added the statement.
“Their permanent vigils, together with a perfect knowledge of the forests in which they lived, made the investigation on the ground enormously difficult.”
The plantations were raided in September. The force said the difficult terrain had meant that 70 officers had been needed to carry the plants to the nearest point reachable by 4x4s.
 “It is the biggest seizure to date of marijuana in Aragón.”


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This project has received funding from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under grant agreement no 688467 VICINITY.
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Bayesian Generalized Linear Regression.
| Version: | 1.0.8 |
| Depends: | R (≥ 3.5.0) |
| Imports: | truncnorm |
| Published: | 2018-11-11 |
| Author: | Gustavo de los Campos, Paulino Perez Rodriguez, |
| Maintainer: | Paulino Perez Rodriguez <perpdgo at colpos.mx> |
| License: | GPL-3 |
| NeedsCompilation: | yes |
| Citation: | BGLR citation info |
| Materials: | ChangeLog |
| CRAN checks: | BGLR results |
| Reference manual: | BGLR.pdf |
| Vignettes: |
BGLR-extdoc |
| Package source: | BGLR_1.0.8.tar.gz |
| Windows binaries: | r-devel: BGLR_1.0.8.zip, r-release: BGLR_1.0.8.zip, r-oldrel: BGLR_1.0.7.zip |
| OS X binaries: | r-release: BGLR_1.0.8.tgz, r-oldrel: BGLR_1.0.7.tgz |
| Old sources: | BGLR archive |
| Reverse imports: | PopVar, synbreed, xbreed |
| Reverse suggests: | BGGE, bWGR, GROAN |
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--- layout: page category : emsim tagline: "necpp MoM antenna simulation of a monopole with radial ground screen wires" tags : [mom, necpp, emtheory] ---
{% include JB/setup %}
return "{0:0.3f}".format(3e8 / 118e6 / 4)
0.636
Approximate monopole length in metres.
from argparse import ArgumentParser from PyNEC import * import logging import matplotlib.pyplot as plt import mpl_toolkits.mplot3d.axes3d as axes3d import numpy as np import os import scipy.optimize import solid as sp import solid.utils import sys
The following are simulation parameters, fundamental constants and derived parameters used by the simulation.
C = 299792458 """float: c (ms^1), universal physical constant for speed of light in a vacuum. """ FREQ = 118.2 """float: f (s^-1), the excitation frequency applied to the test situation. Current value is chosen for the Airband frequency assign to TWR @ SOU """ BANDWIDTH = 20.0 """float: f (s^-1), the bandwidth over which the matching network should operate. Current value covers the Airband """ GROUND_CONDUCTIVITY = 0.002 """float: sigma (S), electrical conductivity of ground. Current value is something like that experienced around the upper Eastern seaboard of the United States. Real valued conductivity is a function of angular frequency (omega) so this value should be altered to something realistic based on our excitation frequency above. """ GROUND_DIELECTRIC = 10 """float: epsilon_r (dimensionless), electrical dielectric 'constant' of ground. Strictly, this is the real part of the ground relative permittivity. """ BASE = 0.1 """float: offset (m) from the ground in the z direction. """ LENGTH = 0.63 """float: total length (m) of monopole in z direction. """ N_CONNECTOR_HOLE_PITCH = 0.01824 """float: hole pitch (m) of panel mount N connector mount holes """ CONDUCTIVITY_SS = 1.45e6 """float: sigma (S), electrical conductivity of stainless steel. Real part of admittivity is conductivity and is a function of angular frequency (omega) so this value should be altered to something realistic based on our excitation frequency above. """ CONDUCTIVITY_CU = 5.96e7 """float: sigma (S), electrical conductivity of copper. """ CONDUCTIVITY_AL = 3.5e7 """float: sigma (S), electrical conductivity of stainless steel. """ R_W = 0.0005 """float: r_w, (m), initial wire radius.""" WAVELENGTH = C / FREQ """float: lambda, (m)""" Z0 = 50 """float: Z_0 (Ohms), feedline characteristic impedance.""" N_SEGMENTS = 30 """int: SEGMENTS , number of segments used in MoM geometry.""" SEGMENTS = 48 """int: SEGMENTS , number of segments used in openscad rendering.""" SIGMA_L = 0.01 """float: SIGMA_L , construction uncertainty for wire lengths.""" DEBUG_LOG = False """bool: DEBUG_LOG , switch on verbose logging."""
Some global variables used to track the optimisation path
z_path = np.ndarray(shape=(1,0), dtype=complex) gamma_path = np.ndarray(shape=(1,0), dtype=float)
This function simply computes \(\Gamma\), the reflection coefficient for a particular antenna scenario given characteristic impedance of the transmission line.
def reflection_coefficient(z, z0): return np.abs((z - z0) / (z + z0)) def vswr(z, z0): Gamma = reflection_coefficient(z, z0) return float((1 + Gamma) / (1 - Gamma)) def mismatch(z, z0): Gamma = reflection_coefficient(z, z0) return 1 - Gamma**2
We construct the antenna geometry, in this case a single wire oriented in the positive z direction with the addition of N radials located equiangularly around the z-axis. We consider the antenna's response to excitation in the absence of a ground plane. We excite the antenna from the first segment (closest to the ground) and compute a radiation pattern and input parameters across a band of frequencies \(\pm 5\)MHz around the design centre frequency.
def geometry(base, length, r_w, radial_len, radial_offset, n_radials, theta_radial, n_segments, construct_nec_context=True, construct_solid_geom=False): if DEBUG_LOG: print("geometry called with {} {} {} {} {} {} {}" .format(base, length, r_w, radial_len, radial_offset, n_radials, theta_radial)) # in case we are constructing neither context = None solid_geom = None curr_tag = 1 if construct_nec_context: #creation of a nec context context = nec_context() context.set_extended_thin_wire_kernel(True) #get the associated geometry geo = context.get_geometry() #ensure that we meet validity criterion for # thin wire aproximation (non-extended thin wire kernel) assert (0.636 / n_segments / r_w) > 8 # TODO: Test the thin wire approximation for the # interconnect wires n_interconnect_segments = 1 #add driven element if DEBUG_LOG: print("Add wire {} {} {} ".format(base, base + length, r_w)) if construct_nec_context: geo.wire(curr_tag, n_segments, 0.0, 0.0, base, 0.0, 0.0, base + length, r_w, 1.0, 1.0) curr_tag += 1 if construct_solid_geom: solid_geom = sp.translate([0,0,length/2 + base])( sp.cylinder(r=r_w, h=length, center=True)) #add radials #radials are spaced equivalent to holes on a square # with 18.24mm pitch which represents a panel mount N connector delta_theta = 2 * np.pi / n_radials theta_radial_rad = theta_radial * np.pi / 180.0 #equivalent radius for connector mount holes (to handle # arbitrary number of radials) connector_radius = N_CONNECTOR_HOLE_PITCH / np.sqrt(2) if DEBUG_LOG: print("delta_theta = {} theta_radial_rad = {} connector_radius = {}" .format(delta_theta, theta_radial_rad, connector_radius)) for n in range(n_radials): #spherical to cartesian transformation # with theta_radial taken as x_connector_offset = connector_radius * np.cos(delta_theta * n) y_connector_offset = connector_radius * np.sin(delta_theta * n) x = radial_len * np.cos(delta_theta * n) * np.sin(theta_radial_rad) \ + x_connector_offset y = radial_len * np.sin(delta_theta * n) * np.sin(theta_radial_rad) \ + y_connector_offset z = radial_len * np.cos(theta_radial_rad) if DEBUG_LOG: print("Add wire {} {} {} -> {} {} {} " .format(x_connector_offset, y_connector_offset, base - radial_offset, x, y, base - radial_offset - z)) if construct_nec_context: geo.wire(curr_tag, n_segments, x_connector_offset, y_connector_offset, base - radial_offset, x, y, base - radial_offset - z, r_w, 1.0, 1.0) curr_tag += 1 if n > 0: # wire from previous to current radial geo.wire(curr_tag, n_interconnect_segments, x_connector_offset_prev, y_connector_offset_prev, base - radial_offset, x_connector_offset, y_connector_offset, base - radial_offset, r_w, 1.0, 1.0) curr_tag += 1 # wire from driven element to current radial geo.wire(curr_tag, n_interconnect_segments, 0.0, 0.0, base, x_connector_offset, y_connector_offset, base - radial_offset, r_w, 1.0, 1.0) curr_tag += 1 # back to the initial radial from the final radial if n == n_radials - 1: geo.wire(curr_tag, n_interconnect_segments, x_connector_offset, y_connector_offset, base - radial_offset, connector_radius, 0, base - radial_offset, r_w, 1.0, 1.0) curr_tag += 1 if construct_solid_geom: delta_theta_deg = delta_theta * 180 / np.pi solid_geom += sp.translate([x_connector_offset, y_connector_offset, base - radial_offset])( sp.rotate([0,theta_radial + 90,delta_theta_deg * n])( sp.translate([0,0,radial_len/2])( sp.cylinder(r=r_w, h=radial_len, center=True)))) if n > 0: # wire from previous to current radial interconnect_len = \ np.sqrt((x_connector_offset_prev \ - x_connector_offset) ** 2 \ + (y_connector_offset_prev \ - y_connector_offset) ** 2) delta_y = y_connector_offset - y_connector_offset_prev delta_x = x_connector_offset - x_connector_offset_prev tan_alpha = delta_y / delta_x alpha = np.arctan(tan_alpha) if delta_x > 0: alpha = alpha + np.pi solid_geom += sp.translate([x_connector_offset, y_connector_offset, base - radial_offset])( sp.rotate([0,90,alpha * 180 / np.pi])( sp.translate([0,0,interconnect_len/2])( sp.cylinder(r=r_w, h=interconnect_len, center=True)))) if n == n_radials - 1: # back to the initial radial from the final radial tan_alpha = (0.0 - y_connector_offset) / \ (connector_radius - x_connector_offset) alpha = np.arctan(tan_alpha) + np.pi solid_geom += sp.translate([connector_radius, 0, base - radial_offset])( sp.rotate([0,90,alpha * 180 / np.pi])( sp.translate([0,0,interconnect_len/2])( sp.cylinder(r=r_w, h=interconnect_len, center=True)))) # wire from driven element to current radial driven_to_radial_len = \ np.sqrt(x_connector_offset ** 2 \ + y_connector_offset ** 2 \ + radial_offset ** 2) solid_geom += sp.translate([0.0, 0.0, base])( sp.rotate([0,90,delta_theta_deg * n])( sp.translate([0,0,driven_to_radial_len/2])( sp.cylinder(r=r_w, h=driven_to_radial_len, center=True)))) x_connector_offset_prev = x_connector_offset y_connector_offset_prev = y_connector_offset if construct_nec_context: # Group I cards context.ld_card(5, 0, 0, 0, CONDUCTIVITY_AL, 0.0, 0.0) # Don't specify a ground for this one #context.gn_card(0, 0, GROUND_DIELECTRIC, GROUND_CONDUCTIVITY, # 0, 0, 0, 0) context.geometry_complete(0) return context, solid_geom
def set_frequencies_linear(context, start_frequency, stop_frequency, count=None, step_size=None): """ If start_frequency does not equal stop_frequency, either count or step should be specified. The other parameter will be automatically deduced """ if start_frequency == stop_frequency: step_size = 0 count = 1 else: # TODO: add some asserts if count is not None: step_size = (stop_frequency - start_frequency) / count else: count = (stop_frequency - start_frequency) / step_size # TODO, what if we don't have nice divisibility here count = int(count) ifrq_linear_step = 0 context.fr_card(ifrq_linear_step, count, start_frequency, step_size) def set_frequency(context, frequency): set_frequencies_linear(context, frequency, frequency) def set_excitation(context): # Group II cards #add a "ex" card to specify an excitation context.ex_card(0, 0, 1, 0, 0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0)
def compute_impedance(context, frequency): set_frequency(context, frequency) set_excitation(context) # Group III cards context.xq_card(0) #get the input_parameters ip = context.get_input_parameters(0) z = ip.get_impedance()[0] return z
def cost_function(x): radial_len = x[2] radial_offset = 0.0 n_radials = 4 theta_radial = x[3] if theta_radial > 80.0 or theta_radial < 0.0: return float('inf') length = x[0] # length >= 0 base = x[1] # base >= 0 if base < 0 or length <= 0.1 or radial_len <= 0.1: return float('inf') context, solid_geom = geometry(base, length, R_W, radial_len, radial_offset, n_radials, theta_radial, N_SEGMENTS, construct_nec_context=True, construct_solid_geom=False) z = compute_impedance(context, FREQ) gamma = reflection_coefficient(z, Z0) global z_path, gamma_path z_path = np.append(z_path, z) gamma_path = np.append(gamma_path, gamma) return vswr(z, Z0)
The main optimisation function below attempts to minimise the reflection coefficient with respect to the driven element length, the length of the radial elements and the angle the radials make with the xy-plane. Unbounded minimisation with a Nelder-Mead simplex algorithm found an alleged minimum of \(\Gamma = 0.02\) with corresponding imput impedance of \(Z = 52.5 - 0.5i \Omega\).
def optimise_antenna(): # Starting value freq = FREQ bounds = [(np.log(0.4), np.log(0.7)), (np.log(0.01), np.log(0.02)), (np.log(0.4), np.log(0.7)), (np.log(15.0), np.log(85.0))] initial_radial_len = 0.6 initial_theta_radial = 22.0 initial_length = 0.6 initial_base = 0.01 initial_geom = np.array([initial_length, initial_base, \ initial_radial_len, initial_theta_radial]) # Carry out the minimization result = scipy.optimize.minimize(cost_function, initial_geom, \ method='Nelder-Mead') print('z_path min={} max={}'.format(np.amin(z_path), np.amax(z_path))) print('gamma_path min={} max={}'.format(np.amin(gamma_path), np.amax(gamma_path))) length = result.x[0] base = result.x[1] radial_len = result.x[2] radial_offset = 0.0 n_radials = 4 theta_radial = result.x[3] return base, length, R_W, radial_len, \ radial_offset, n_radials, theta_radial
The geometry yielding these simulated parameters is shown in figure monpole_radials_geom . It has a driven element length of \(l_d = 0.701\)m and radials of length \(l_r = 0.348 \)m that make angle \(\theta _r = 46.86^{\circ} \) with the xy-plane.
Figure 1: Optimised antenna geometry
def plot_radiation_pattern(context): # Group III cards #add a "rp" card to specify radiation pattern sampling parameters # and to cause program execution context.rp_card(calc_mode=0, n_theta=180, n_phi=720, output_format=0, normalization=0, D=0, A=0, theta0=0.0, delta_theta=0.5, phi0=0.0, delta_phi=0.5, radial_distance=0.0, gain_norm=0.0) context.xq_card(0) #get the radiation_pattern rp = context.get_radiation_pattern(0) # Gains are in decibels gains_db = rp.get_gain() print('gains_db.shape = {0}'.format(gains_db.shape)) gains = 10.0**(gains_db / 10.0) thetas = rp.get_theta_angles() * 3.1415 / 180.0 phis = rp.get_phi_angles() * 3.1415 / 180.0 print('thetas.shape = {0} [{1},{2}]' .format(thetas.shape, thetas[0], thetas[len(thetas)-1])) print('phis.shape = {0} [{1},{2}]' .format(phis.shape, phis[0], phis[len(phis)-1])) print('gains \in [{0},{1}]'.format(np.amin(gains), np.amax(gains))) fig = plt.figure() # Plot 2d polar plots ax = fig.add_subplot(2, 2, 1, projection='polar') ax.plot(phis, gains[180//4 - 1,:], color='r', linewidth=3) ax.grid(True) ax.set_rmax(1.0) ax.set_title("Gain at an elevation of 22.5 degrees", va='bottom') ax = fig.add_subplot(2, 2, 2, projection='polar') ax.plot(np.concatenate((thetas, np.pi - thetas)), np.concatenate((gains[:,0], gains[:,360])), color='r', linewidth=3) ax.grid(True) ax.set_rmax(1.5) ax.set_thetalim(thetamin=0.0, thetamax=180.0) ax.set_thetagrids([0.0, 30.0, 60.0, 90.0, 120.0, 150.0, 180.0]) ax.set_title("Gain sliced through xz-plane", va='bottom') ax = fig.add_subplot(2, 2, 3, projection='polar') ax.plot(phis, gains[180//2 - 1,:], color='r', linewidth=3) ax.grid(True) ax.set_rmax(1.0) ax.set_title("Gain at an elevation of 45 degrees", va='bottom') ax = fig.add_subplot(2, 2, 4, projection='polar') ax.plot(np.concatenate((thetas, np.pi - thetas)), np.concatenate((gains[:,180], gains[:,540])), color='r', linewidth=3) ax.grid(True) ax.set_rmax(1.5) ax.set_thetalim(thetamin=0.0, thetamax=180.0) ax.set_thetagrids([0.0, 30.0, 60.0, 90.0, 120.0, 150.0, 180.0]) ax.set_title("Gain sliced through yz-plane", va='bottom') plt.savefig('../images/monopole_radials_polar_plots.png') plt.gcf().clear() # Plot 3d radiation pattern. R = gains.T THETA, PHI = np.meshgrid(thetas, phis) print('THETA.shape = {0}'.format(THETA.shape)) print('PHI.shape = {0}'.format(PHI.shape)) X = R * np.sin(THETA) * np.cos(PHI) Y = R * np.sin(THETA) * np.sin(PHI) Z = R * np.cos(THETA) ax = fig.add_subplot(1, 1, 1, projection='3d') # Plot the surface. ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=plt.get_cmap('jet'), linewidth=0, antialiased=False, alpha=0.5) ax.grid(True) ax.set_title("3d gain plot", va='bottom') plt.savefig('../images/monopole_radials_3d_gain_plot.png') #plt.show() plt.gcf().clear() plt.close(fig)
Figure 2: 3d gain plot pattern - radiated
And polar plots of gain at various elevation angles
Figure 3: Polar plots of gain pattern at various elevation angles
For our optimised antenna, how does the effective match to a \(50 \Omega \) load vary with frequency?
def compute_vswr(context): count = 50 set_frequencies_linear(context, FREQ - BANDWIDTH/2.0, FREQ + BANDWIDTH/2.0, count=count) set_excitation(context) context.xq_card(0) # Execute simulation freqs = [] vswrs = [] for idx in range(0, count): ipt = context.get_input_parameters(idx) z = ipt.get_impedance() freqs.append(ipt.get_frequency() / 1000000) vswrs.append(vswr(z, Z0)) return freqs, vswrs
def plot_vswr(freqs, vswrs): fig = plt.figure() # Plot cartesian, linear scale ax = fig.add_subplot(1, 1, 1, projection='rectilinear') ax.plot(freqs, vswrs, color='r', linewidth=3) ax.grid(True) ax.set_xlabel("Frequency (MHz)") ax.set_ylabel("VSWR") ax.set_title("VSWR vs. frequency (MHz) for 50 ohm system", va='bottom') plt.savefig('../images/monopole_radials_vswr.png') plt.gcf().clear()
The plot in figure monopole_radials_vswr shows the response of the optimised antenna across the Airband.
Figure 4: VSWR for 50 ohm system
For a real-world antenna with construction defects, what is the effect on antenna impedance of the driven element and radials having lengths that deviate from the ideal computed value? We attempt to compute the partial derivative of \(Z\) with respect to \(l_d\) by taking finite differences around the optimised value of \(l_d\) and estimate our construction tolerance \(\sigma l = 0.01\). For some small \(h\) we have
\begin{equation} \frac{\partial Z}{\partial l_d} \approx \frac{Z(l_d + h) - Z(l_d - h)}{2h} \end{equation}def compute_dz_dl_e(base, length, r_w, radial_len, radial_offset, n_radials, theta_radial, h): z_n = [] for k in range(2): context, solid_geom = geometry(base, length + ((-1) ** (k + 1) * h), r_w, radial_len, radial_offset, n_radials, theta_radial, N_SEGMENTS, construct_nec_context=True, construct_solid_geom=False) set_frequency(context, FREQ) set_excitation(context) context.xq_card(0) # Execute simulation ip = context.get_input_parameters(0) z = ip.get_impedance() z_n.append(z) print(z) partial_z_l_e = (z_n[0] - z_n[1]) / (2 * h) return partial_z_l_e
def compute_bp_matching_network(z0, zl): return 0.0
def main(): parser = ArgumentParser(description="Optimise monopole and compute matching network.") args = parser.parse_args() base, length, r_w, radial_len, \ radial_offset, n_radials, theta_radial = optimise_antenna() # base = 0.007 # length = 0.63 # radial_len = 0.6 # radial_offset = 0.0 # n_radials = 4 # theta_radial = 45.0 # r_w = R_W print("\nOptimisation result\n") print("final geometry {} {} {} {} {} {} {}" .format(base, length, r_w, radial_len, radial_offset, n_radials, theta_radial)) context, solid_geom = geometry(base, length, r_w, radial_len, radial_offset, n_radials, theta_radial, N_SEGMENTS, construct_nec_context=True, construct_solid_geom=True) out_dir = sys.argv[1] if len(sys.argv) > 1 else os.curdir file_out = os.path.join(out_dir, 'monopoleradials.scad') sp.scad_render_to_file(solid_geom, file_header='$fn = {};'.format(SEGMENTS), include_orig_code=True) print("{}: SCAD file written to: \n{}".format(__file__, file_out)) z = compute_impedance(context, FREQ) print("Impedance at base={0:.2f}, length={1:.2f}" " : ({2:6.1f},{3:+6.1f}i) Ohms" .format(base, length, z.real, z.imag)) print("Reflection coefficient Gamma={0:0.2f}" .format(reflection_coefficient(z, Z0))) print("VSWR @50 ohm ={0:0.2f}" .format(vswr(z, Z0))) print("z: {0}".format(z)) set_frequency(context, FREQ) set_excitation(context) plot_radiation_pattern(context) #compute_bp_matching_network(Z0, zl) context, solid_geom = geometry(base, length, r_w, radial_len, radial_offset, n_radials, theta_radial, N_SEGMENTS, construct_nec_context=True, construct_solid_geom=False) freqs, vswrs = compute_vswr(context) plot_vswr(freqs, vswrs) # sensitivity to driven element length h = 0.005 dz_dl_e = compute_dz_dl_e(base, length, r_w, radial_len, radial_offset, n_radials, theta_radial, h) print("dl10: {0}".format(dz_dl_e * SIGMA_L)) return 0
python monopoleradials.py 2>&1 true
z_path min=(52.80634378320185-0.6129484587169558j) max=(84.52668723622808+26.65369957814016j) gamma_path min=0.02789603132986227 max=0.4041803349448599 Optimisation result final geometry 3.665433178414191e-14 0.702468950751411 0.0005 0.3473645774547176 0.0 4 39.05842183497735 monopoleradials.py: SCAD file written to: ./monopoleradials.scad Impedance at base=0.00, length=0.70 : ( 52.8, -0.5i) Ohms Reflection coefficient Gamma=0.03 VSWR @50 ohm =1.06 z: (52.81836735879712-0.5326700896016593j) gains_db.shape = (180, 720) thetas.shape = (180,) [0.0,1.5620236111111114] phis.shape = (720,) [0.0,6.274273611111111] gains \in [5.1826184832943545e-06,1.4704535189360104] THETA.shape = (720, 180) PHI.shape = (720, 180)


Generic Object pooling API with several implementations.
The org.apache.commons.pool
package defines a simple
interface for a pool of object instances, and a handful of base
classes that may be useful when creating pool implementations.
The api supports pooling of unique objects which can be requested
via a key as well as pools where all objects are equivalent.
The org.apache.commons.pool.impl
package contains
several pool implementations.
{@link org.apache.commons.pool.impl.StackObjectPool StackObjectPool}
is useful for supporting reuse of a limited number of instances while
allowing new instances to be created as needed to support high demand.
{@link org.apache.commons.pool.impl.GenericObjectPool
GenericObjectPool} has many configuration options and can support
a limited set of objects such as would be useful in a database
connection pool.
{@link org.apache.commons.pool.impl.SoftReferenceObjectPool
SoftReferenceObjectPool} has no limit on the number of objects in the
pool, but garbage collector can remove idle objects from the pool as
needed. There are also keyed versions of the first two.
Here is a simple example of pooling HashMap
's. First
create an {@link org.apache.commons.pool.ObjectPoolFactory
ObjectPoolFactory}
public class HashMapFactory extends {@link org.apache.commons.pool.BasePoolableObjectFactory BasePoolableObjectFactory} { /** * Creates an instance that can be returned by the pool. * @return an instance that can be returned by the pool. */ public Object makeObject() throws Exception { return new HashMap(); } /** * Uninitialize an instance to be returned to the pool. * @param obj the instance to be passivated */ public void passivateObject(Object obj) throws Exception { Map map = (Map)obj; map.clear(); } }
A class that makes frequent use of a Map could then use a pool as shown below:
public class Foo { private {@link org.apache.commons.pool.ObjectPool ObjectPool} pool; public Foo() { {@link org.apache.commons.pool.PoolableObjectFactory PoolableObjectFactory} factory = new HashMapFactory(); pool = new StackObjectPool(factory, 1000); } public doSomething() { ... Map map = null; try { map = (Map)pool.borrowObject(); // use map ... } finally { if (map != null) { pool.returnObject(map); } } ... } }
The above example shows how one would use an {@link org.apache.commons.pool.ObjectPool ObjectPool}. The other supplied implementations or another special purpose pool would be used similarly.