Wolfram Blog http://blog.wolfram.com News, views, and ideas from the front lines at Wolfram Research. Thu, 03 Sep 2015 19:39:28 +0000 en hourly 1 http://wordpress.org/?v=3.2.1 New in the Wolfram Language: ColorBalance http://blog.wolfram.com/2015/09/02/new-in-the-wolfram-language-colorbalance/ http://blog.wolfram.com/2015/09/02/new-in-the-wolfram-language-colorbalance/#comments Wed, 02 Sep 2015 19:01:05 +0000 Giulio Alessandrini http://blog.internal.wolfram.com/?p=27426 I’ve taken pictures numerous times, either with a camera or with my phone, only to find out that the colors were completely off—they had bluish, reddish, or even greenish tints. Before I started working on image and color processing, this was quite mysterious to me. Moreover, I’d always noticed on my cameras a white balance setting that, when played with, produced results very much like my skewed-color photographs. Could it be these two were related?

That camera setting is indeed the key to correcting a color cast, and it has been added to the Wolfram Language with the ColorBalance function.

Here is a simple example of how it works:

Applying ColorBalance to photos

Notice how the green colors of the original picture have been corrected and the overall color distribution is now more balanced. This is exactly what is happening behind the scenes: the pixels in the image have been rescaled so that the average hue is a neutral color (i.e., a shade of gray).

Here is what ColorBalance produces when applied to images that I found searching for color cast on Google:

Applying ColorBalance to images on Google

Blue churchGreen treesYellow lab

Images after ColorBalance is applied

Without any other input, ColorBalance will try to achieve this neutral result, but in some situations you may know additional information that can be used to achieve a more accurate color balance. It’s very easy to pass that information to the function.

For instance, you can balance the apple image by specifying exactly which color you want to be considered neutral:

Balancing color in image by specific color

Or, use a mask to indicate the pixels that should be used as a reference:

Using a mask to use specific pixels as reference

You can even ask to map the reference pixels to the chromaticity of a specific color in order to add an intentional color cast to your image:

Adding color cast to your image

The option to designate a neutral area is really important. Take for example this underwater picture with a strong blue cast:

Underwater picture with strong blue cast

This is what happens when you balance it using brighter and brighter parts of the image:

Using brighter parts to balance the image

Because the image does not contain a uniform distribution of colors, we get a good balance only when we consider the brightest pixels. In this case, a better approach is to disregard the rest of the image and build the transformation by rescaling these pixels to their neutral values, that is, white, which can be done easily by changing the default method of the function:

Rescaling pixels to their neutral values

There are many more situations where you may want to slightly adjust the tones of you pictures—or even modify them entirely to create a unique effect, for example, mapping white in this image to a warmer color temperature of 3,200 kelvins:

Mapping white to a color temperature of 3,200 kelvins

Correcting colors and illumination can be as simple as multiplying your image by a number, or it can require complex color space transformations and color temperature estimation. ColorBalance is built to handle all these cases and to offer enough flexibility to implement your custom balancing procedures.

ColorBalance is part of our effort to enhance the computational photography capabilities of the Wolfram Language. ColorBalance is supported in Version 10.2 of the Wolfram Language and Mathematica, and is rolling out soon in all other Wolfram products.

Download this post as a Computable Document Format (CDF) file.


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Wolfram Technology Conference 2015: Pushing the Boundaries of Computation http://blog.wolfram.com/2015/08/28/wolfram-technology-conference-2015-pushing-the-boundaries-of-computation/ http://blog.wolfram.com/2015/08/28/wolfram-technology-conference-2015-pushing-the-boundaries-of-computation/#comments Fri, 28 Aug 2015 15:48:58 +0000 Emily Suess http://blog.internal.wolfram.com/?p=27403 Wolfram Technology Conference 2015

The Wolfram Technology Conference 2015 is just a few weeks away, and we’re excited to demonstrate the latest in cloud computing, interactive deployment, mobile devices, and more as we explore how Wolfram technologies are pushing the boundaries of computation.

If you haven’t already reserved your spot for this year’s conference, there’s still time to register.

As the conference draws closer, we’re putting finishing touches on the event schedule, which will include in-depth presentations, hands-on workshops, “Meet-Ups” for attendees with similar interests, recognition of 2015 Wolfram Innovator Award winners, and lots of networking opportunities. Here are some of the topics we’ll be highlighting October 20–22:

  • Artificial intelligence
  • Cloud development
  • Computational finance
  • Connected devices
  • Data science
  • Educational technology
  • Interactive media
  • Machine learning
  • Social media analysis
  • System modeling and simulation
  • Visualization

To help you make the most of your trip, we’re offering pre-conference training workshops on Monday, October 19. The lineup includes:

  • The Wolfram Language: Image Processing Concepts and Methods
  • The Wolfram Language: Visualization Fundamentals
  • Wolfram SystemModeler: An Introduction
  • Wolfram SystemModeler: Analysis and Model Design with Mathematica
  • The Wolfram Language: Programming Fundamentals

As a conference participant, you’ll be among the first to experience our newest releases and cutting-edge technologies, including the Wolfram Cloud, SystemModeler 4.1, and the Wolfram Data Drop, as well as new Wolfram Language functionalities like ImageIdentify and GrammarRules. We’re also bringing back the popular connected devices playground and the One-liner programming competition.

It’s the perfect opportunity to rub elbows with senior Wolfram developers, connect with like-minded colleagues, and see how Wolfram’s latest innovations will make you more efficient and productive. We hope to see you in October!


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Releasing the OPCClassic Library http://blog.wolfram.com/2015/08/26/releasing-the-opcclassic-library/ http://blog.wolfram.com/2015/08/26/releasing-the-opcclassic-library/#comments Wed, 26 Aug 2015 18:10:11 +0000 Patrik Ekenberg http://blog.internal.wolfram.com/?p=27219 Wouldn’t it be great if you could easily connect your simulation models to your existing infrastructure? Whether you are working in industries such as oil and gas, industrial energy, or life sciences, connecting to your processes in order to monitor and control them is vital.

The OPC (Object Linking and Embedding for Process Control) standard has been developed by industry and the OPC Foundation just for that purpose. OPC is a set of data transfer standards for multi-vendor, multi-platform, secure, and reliable interoperability in industrial automation:

OPC illustarted as a set of data transfer standards

Today we’re introducing the OPCClassic library for Wolfram SystemModeler. The OPC library contains a full set of components for connecting to physical systems using the OPC Classic standard. The library makes it possible to connect to one or more OPC servers, and thereby your real-world processes.

Using the library to connect to servers

With the OPC library, you can set up communication with servers using easy drag-and-drop, just as with any other SystemModeler library. This makes it possible to incorporate production data from the real world into your models (for example, as block inputs or reference signals); write model data directly to OPC servers; monitor production data in real time; perform data analyses; and design control systems.

To illustrate the use of the library, I’ll use the master thesis project that I did for Wolfram MathCore and Linköping University this spring, in which I controlled a bioethanol production process. Here is a picture of me working with my reactor:

Patrik working with the reactor

Just like when you bake bread, to make ethanol you need mainly two things: sugar and yeast. Yeast are unicellular organisms that can multiply quickly provided the right conditions. Most importantly, the yeast needs sugar and oxygen in order to grow. When you bake bread, sugars (mainly maltose) are produced from the flour, allowing the yeast to grow.

The more oxygen you add, the faster the yeast can grow, while low levels of oxygen result in slower yeast growth but produce more ethanol. When producing bread, you want to maximize the yeast growth, but when producing ethanol you want to maximize the production of ethanol. So the main difference between the two processes is how much oxygen you supply.

When baking you have a lot of excess oxygen provided through the air, while in ethanol production the amount of added oxygen is controlled more rigorously.

The model diagram below is a model of the bioethanol process, developed with the BioChem library:

Model of bio ethanol process

To produce ethanol, yeast is combined with sugar-rich substrates (glucose and maltose) that over time are converted to ethanol, while the yeast cultures (biomass) steadily increase. The process is controlled by setting the amount of oxygen that is added. As mentioned above, low levels of oxygen will favor ethanol production and high levels will favor yeast growth.

In order to optimize the process, I wanted to investigate the use of model predictive control (MPC) to control the amount of oxygen supplied and hence the ethanol production. My goal was to construct a controller that worked on an industrial scale for a partner company, but that could be piloted in a lab setting.

To test my controller I used the model above and connected it to the rest of the system using the OPCClassic library:

Illustration of connecting model of bio ethanol process using OPC Classic Library

The MPC controller (bottom left in the picture above), implemented in Mathematica, controlled the amount of oxygen in the batch by adjusting the stirrer speed, while the online and high-performance liquid chromatography parts communicated with measurement equipment (in this case, measurements made in the model). With this setup, I could run simulations to see how the system behaved:

Running simulations to see how the system behaved

Now that I had managed to control the process using the model, it was time to switch in the real system. Instead of getting the concentration levels from my model, I took continuous measurements of yeast, sugar, and ethanol concentrations and supplied them to the controller. The system was run in real time and was set to communicate with the Mathematica-based controller through OPC. The figure below is taken from one experiment that I ran in the lab.

Experiment run in the lab

The leftmost graph shows the concentration of glucose (solid line), ethanol (dotted line), maltose (solid line, circles), and biomass (solid line, squares). The rightmost graph shows the dissolved oxygen (solid line) and temperature (dotted line).

You can see from the graph that the oxygen concentration is relatively close to a step function. In fact, the standard industrial bioethanol process today is to start by stirring air into the yeast and sugar mixture, allowing the yeast to grow and multiply, and then completely shut off the oxygen in order to produce the desired ethanol. I got similar behavior using the model predictive control.

There are many factors that might vary from one batch to another, including the concentrations of the sugars, the starting pH, and the concentrations of various nutrients. A model predictive controller should adjust to these varying conditions in order to produce more ethanol.

Now that we have demonstrated in the concept study that the OPCClassic library makes it easy to model the process, a cooperation between the companies Agroetanol and Iggesund, Linköping University, and Wolfram MathCore will continue with a full-scale project partially funded by Sweden’s innovation agency, VINNOVA.

Learn More

If you would like to try modeling with OPCClassic, it is available for trial or purchase in the SystemModeler Library Store. From the website you can also download a free trial of SystemModeler. For more ideas on what you can do with OPC, check out our collection of SystemModeler examples or visit our online Documentation Center, which hosts the full OPCClassic library documentation.


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Help Fix Maths Education at the Computer-Based Maths Education Summit http://blog.wolfram.com/2015/08/24/help-fix-maths-education-at-the-computer-based-maths-education-summit/ http://blog.wolfram.com/2015/08/24/help-fix-maths-education-at-the-computer-based-maths-education-summit/#comments Mon, 24 Aug 2015 07:00:33 +0000 Richard Asher http://blog.internal.wolfram.com/?p=27353 Computer-Based Maths Education Summit with London skyline

When was the last time you had to solve a quadratic equation by hand? If, like me, you haven’t needed that particular skill since high school, then you’ve probably wondered what all that fuss was about! And it’s a good question: why did we spend so much time on those puzzling, formulaic second-degree beasts, using up pencils and erasers like they were going out of fashion, just to find the value of x?

The truth is, in the real, working world of 2015, the value of that pesky x will invariably be found by a computer. The sooner education acknowledges this fact, the better. So says Conrad Wolfram, whose Computer-Based Maths (CBM) initiative is using Wolfram technologies to bring computers and coding into mainstream maths curricula around the world.

Conrad Wolfram, quadratic formula, and former Estonian Education Minister Jaak Aaviksoo

The fourth Computer-Based Maths Education Summit in London this November is planned to be a highly interactive meeting of bright minds—it will feature lively debate and small group sessions, as well as some exciting speakers. These speakers include former Estonian Education Minister Jaak Aaviksoo, Raspberry Pi CEO Eben Upton, and Computing At School Chair Simon Peyton Jones.

The CBM Summit will take place November 19–20 at the celebrated Royal Institution of Great Britain in London’s Mayfair district. The historic building, which has housed the Royal Institution since 1799, is associated with some of the greatest names in science, notably Michael Faraday and Sir Humphry Davy. If you’d like to join them in breaking new ground, register for the summit now.

The theme for this year’s CBM Summit is “How can CBM be practically embedded into the maths ecosystem?” You can read more details on the topics here, and if you’re interested in submitting a paper, you can send your proposal to summit@computerbasedmath.org. Hope you can make it!


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Summer Schools: Wolfram Science and Wolfram Innovation http://blog.wolfram.com/2015/08/19/summer-schools-wolfram-science-and-wolfram-innovation/ http://blog.wolfram.com/2015/08/19/summer-schools-wolfram-science-and-wolfram-innovation/#comments Wed, 19 Aug 2015 17:49:48 +0000 Todd Rowland http://blog.internal.wolfram.com/?p=27338 For three weeks this past July, Wolfram held the annual Wolfram Summer School for over 60 students from around the world. They came to work on projects ranging from aperiodic hexagonal tessellations to computer language grammars to political sentiment microsites. The overarching theme was entrepreneurial science. Participants employed cutting-edge computational tools like Wolfram Programming Cloud, machine learning, and a whole variety of new functions from Version 10.2 of the Wolfram Language.

Quadropter

This year’s main innovation was a new track, the Wolfram Innovation Summer School, for making products or starting companies not necessarily related to Wolfram Science or the book A New Kind of Science. Those projects culminated in microsites like MyPiDay.com.

Projects based on Wolfram Science pushed boundaries and frontiers—literally doing so in the case of the rule 30 central column, which went out to eight million steps. That’s eight times further than the result in the book.

portBinary graph

Several physics projects on quantum mechanics, generalized thermodynamics, and network universes were submitted, along with other pure projects that explored new topics in cellular automata, boids, and sandpiles. Some projects even investigated the structure of computer languages, including the Wolfram Language itself. Check out our Tumblr for a collage from the poster session.

Collage of Summer School projects

Of course, there was more going on than just these projects. Live experiments by Stephen Wolfram and lectures from his staff, covering not just A New Kind of Science but also many of the new technologies from Wolfram, brought the whole School together. It was a jam-packed three weeks, but we’re already looking forward to what new and exciting ideas next year will bring.

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The Winner of the GOP Presidential Debate http://blog.wolfram.com/2015/08/13/the-winner-of-the-gop-presidential-debate/ http://blog.wolfram.com/2015/08/13/the-winner-of-the-gop-presidential-debate/#comments Thu, 13 Aug 2015 18:14:30 +0000 Jonathan Wallace http://blog.internal.wolfram.com/?p=27279 A few days ago, Fox News hosted the first presidential primary debate of 2016. The candidates met onstage, vying for support from the GOP electorate. Among the cacophony and crafty messaging, a truly artful winner has emerged: word clouds.

The WordCloud function (1 of 5000+ functions) in the Wolfram Language allows anyone to visualize words, sized by their frequency in a text. With a mere line of code, you can create a compelling word cloud graphic from data, text, or URLs.

But don’t take my word for it; let’s make the WordCloud function earn your support. Here you’ll find each candidate’s transcript from the debate in WordCloud form (click any image to enlarge it):

Jeb Bush WordCloud Ben Carson WordCloud Chris Christe WordCloud Ted Cruz WordCloud Mike Huckabee WordCloud John Kasich WordCloud Rand Paul WordCloud Marco Rubio WordCloud Donald Trump WordCloud Scott Walker WordCloud Image Map

If WordCloud hasn’t won your vote yet, stay tuned for our new site—newscloud.wolfram.com—where we hope to earn your support with visualizations of future debates, current events, and more.


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New in the Wolfram Language: TextCases http://blog.wolfram.com/2015/08/12/new-in-the-wolfram-language-textcases/ http://blog.wolfram.com/2015/08/12/new-in-the-wolfram-language-textcases/#comments Wed, 12 Aug 2015 14:51:27 +0000 Gopal Sarma http://blog.internal.wolfram.com/?p=27232 The Wolfram Language has had extensive support for string manipulation since Mathematica 5, and in Version 10 it provided uniform symbolic access to a huge repository of computable data via the Wolfram Knowledgebase. Taking advantage of both of these fundamental capabilities, along with new machine learning functionality with Classify and Predict, we’re excited to be making further inroads into the rich domains of natural language processing and text analytics with TextCases, new in Version 10.2.

TextCases, like its sister functions Cases and StringCases, finds instances of patterns in a given input. Whereas Cases operates on Wolfram Language expressions and StringCases on strings, TextCases assumes that the input is human understandable text, from which one can extract known syntactic and semantic entities. These include basic textual types such as words, sentences, and paragraphs, but also more sophisticated semantic types such as countries, cities, and numbers.

As a simple example, let’s use TextCases to find instances of countries in a sentence:

Finding countries in a sentence using TextCases

Since the Wolfram Knowledgebase includes computable entities for countries, you can specify that TextCases returns the corresponding computable Entity objects, rather than the matched string:

TextCases returning the corresponding Entity

Let’s now explore a simple natural language processing workflow. Natural language processing often begins with basic segmentation tasks, such as splitting text at word and sentence boundaries. To demonstrate, let’s first use WikipediaData to grab the plain text of an article:

Using WikipediaData to grab plain text of an article

Here are the article’s first three sentences:

Article's first three sentences

And here are its first 10 words:

First ten words

Words and sentences are just two of the many different types of textual units that TextCases is able to handle. As a short exercise in exploratory data analysis, let’s find instances of numbers (expressed as digit strings or scientific notation) in the same Wikipedia article:

Instances of numbers in Wikipedia article

TextCases has a convenient syntax for drilling down even further into a body of text. For example, you can use the new symbol Containing to find, say, all sentences that contain numbers.

Using Containing to find all sentences that contain numbers

Here are the first three sentences Containing found:

First three sentences Containing found

There are many other uses for TextCases, such as finding email addresses, telephone numbers, or paragraphs of text of a given language. And using the symbols Containing and Alternatives, one can create complex queries for in-depth data exploration and data analysis.

But TextCases is just the beginning. Stay tuned: many additional useful tools for natural language processing and text analytics will be added to the Wolfram Language soon.

TextCases is supported in Version 10.2 of the Wolfram Language and Mathematica, and is rolling out soon in all other Wolfram products.

Download this post as a Computable Document Format (CDF) file.


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2015 Wolfram Summer Camps Exceed Expectations http://blog.wolfram.com/2015/08/05/2015-wolfram-summer-camps-exceed-expectations/ http://blog.wolfram.com/2015/08/05/2015-wolfram-summer-camps-exceed-expectations/#comments Wed, 05 Aug 2015 18:50:41 +0000 Adriana O'Brien http://blog.internal.wolfram.com/?p=27083 We say it every year, because it is true, but once again this year’s Wolfram Summer Camps were the most successful yet. Thirty-eight students from seven different countries attended our camps at Bentley University this July. Students came to camp with some prior programming experience, but most had little or no familiarity with the Wolfram Language. In nine short days, however, they were able to produce amazing results.

Collage of Summer Camp students

This summer was a little different than the past three years. In addition to offering the Mathematica Summer Camp, we introduced the Wolfram Tech Innovation Summer Camp.

Mathematica Summer Camp and Wolfram Tech Innovation Summer Camp

Participants in both camps spent much of their time with each other, each morning concentrating on project time and Wolfram Language training. The Tech Innovation students, however, attended three afternoon sessions that focused on entrepreneurialism and the future of technology, given by Stephen Wolfram.

Stephen Wolfram speaking with students

The Mathematica Summer Camp students worked on exploring a computationally interesting phenomenon, while the Tech Innovation students created more applied programs with FormFunction and CloudDeploy.

We want to share some of this phenomenal work with all of you and show off their accomplishments, but with thirty-eight projects there is just too much for one blog post. This collage represents the completed Demonstrations and web deployments created during the camps. You can visit the Tumblr page to check out each one. Follow us there for updates and to see the full scope of the students’ creativity, as some are still working hard to perfect their projects.

Summer Camp projects

For the closing ceremonies, we analyzed the student projects and discovered that the two camps used around 450 built-in Wolfram Language functions over 5,300 times. To get an idea of the most popular functions, here’s a graph of the top 15 used:

Graph of top 15 functions used at Wolfram Summer Camp

We also generated a word cloud with the top 200 functions that the students used in their projects. While Thickness was the most popular, the students employed a variety of functions to create their projects.

Top 200 functions WordCloud

Thank you to everyone at Wolfram Research, especially the mentors for their dedication to seeing the students succeed, and congratulations to the participants of the 2015 Wolfram Summer Camps! You did an amazing job, and we are extremely proud of every one of you.

2015 Summer Camp students with Stephen Wolfram

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Over the Moon for Guinness World Records’ Diamond Anniversary http://blog.wolfram.com/2015/07/29/over-the-moon-for-guinness-world-records-diamond-anniversary/ http://blog.wolfram.com/2015/07/29/over-the-moon-for-guinness-world-records-diamond-anniversary/#comments Wed, 29 Jul 2015 19:01:38 +0000 Jenna Giuffrida http://blog.internal.wolfram.com/?p=27137 For the record, let’s start here.

First publication of Guiness World Records

Next month, Guinness World Records will officially celebrate its 60th anniversary as the leading authority on “record-breaking achievement.” A long-cherished favorite for holiday gifting and the coffee table, Guinness World Records not only provides a unique collection of knowledge but also encourages people to challenge the application of those facts. That’s not limited to the public, either; GWR itself holds the record for best-selling annual publication, a record set in 2013 that has yet to be overthrown.

As it’s their diamond anniversary, and such things should be commemorated, we wanted to join in with some unique, Wolfram|Alpha knowledge fun. We can tell you what the world’s largest cut diamond is or who currently holds the title of fastest human; we can even put some of these records to the test.

Speed of bullet vs fastest human speed

Looks like Superman will have to hold onto the record of “faster than a speeding bullet” for a little while longer.

Guinness tells us that the largest living tree is Hyperion, a redwood in California, and that the tallest living man is Sultan Kösen. How many Sultans would we need to reach the top of Hyperion?

How many Sultans to reach top of Hyperion

Ever wondered how old the longest-lived animal was? Her name was Ming, and she was a mollusc (bet you thought it would be a tortoise!).

How about something more data driven? What is the most frequent word in the longest text, the Holy Bible (KJV)?

Most frequent word in the longest text

“The”—shocking. Maybe that’s too pedestrian. In this space age when Pluto can send love across the galaxy (who knew the planet had such a big heart?), maybe we should be taking our records to the stars. What if Javier Sotomayor took his 82kg self and high jumped on the moon?

Javier Sotomayor high jump on the moon

It may be purely hypothetical, but at six times higher than the 1993 record of 2.45m, it would certainly be a giant leap for mankind! Perhaps Guinness should consider taking their 60th anniversary edition out of this world to continue inspiring all of us to dream bigger and reach higher.

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Wolfram Community Featured Posts: Reddit’s 60-Second Button, Raspberry Pi, and More http://blog.wolfram.com/2015/07/21/wolfram-community-featured-posts-reddits-60-second-button-raspberry-pi-and-more/ http://blog.wolfram.com/2015/07/21/wolfram-community-featured-posts-reddits-60-second-button-raspberry-pi-and-more/#comments Tue, 21 Jul 2015 16:46:18 +0000 Emily Suess http://blog.internal.wolfram.com/?p=27022 Wolfram Community connects you with users from around the world who are doing fun, innovative, and useful things with the Wolfram Language. From game theory and connected devices to astronomy and design, here are a few posts you won’t want to miss.

Reddit 60-second button

Are you familiar with the Reddit 60-second button? The Reddit experiment was a countdown that would vanish if it ever reached zero. Clicking a button gave the countdown another 60 seconds. One Community post brings Wolfram Language visualization and analysis to Reddit’s experiment, which has sparked questions spanning game theory, community psychology, and statistics. David Gathercole started by importing a dataset from April 3 to May 20 into Mathematica and charted some interesting findings. See what he discovered and contribute your own ideas.

Frank Kampas solved a New York Times 4×4 KENKEN using the Wolfram Language, and others offered suggestions for making that code even faster. One Community member wondered if the puzzle itself could be automatically and randomly generated to make a complete Demonstrations Project app. Do you have a solution for solving 6×6 grids? You can chime in here.

RPi GSM

Alfonso Garcia-Parrado used the Wolfram Language with Raspberry Pi and the GSM module SIM908 to build a geo-location device with just a few lines of code. Check out his explanation to give it a try for yourself, or browse other topics related to Raspberry Pi and connected devices.

Flags from around the world

Inspired by the TED talk “Why City Flags May Be the Worst-Designed Thing You’ve Never Noticed,” Bernat Espigulé Pons sorted the flags of every country into similar groups, showing how simplistic designs increase the odds of duplicating flags. Browse the images for a peek at the similarities among country flags and snag the similarity graph of flags in CDF format.

Join Wolfram Community today to explore these and other topics, share the projects you’re working on, and collaborate with other Wolfram technology enthusiasts.

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