For this portion of the HoL, we will be bypassing the Raspberry Pi portion all together and go straight to provisioning Resources and using a local app to simulate the telemetry data. You can alternatively still use a Raspberry Pi as this session was intended, but for time purposes, that can be skipped and the app below can be used for simulating data for a live dashboard. Continue reading →
This article is for a hands on lab in IoT I am running. You will find full documentation at the provided link. Also, you may run into a scenario in which the MCP 3008 code does not work. If this is the case, you are likely using an older version of the nugget package which contains it or the new version has not been pushed yet. You can find the code for the MCP 3008 below.
Here is the link: https://onedrive.live.com/redir?resid=BA8DC4B28555902A!3406&authkey=!AGawE2hfolHvC8s&ithint=file%2cpdf
These days I need to make videos instead of written articles, so I am going to post a few of those here.
In this video we will do an initial exploratory analysis on a water flow data set that came from a prototype that I built. The prototype consists of a water pump, a valve and a flow meter. The data set exists in SQL Azure. We will use R and R Studio to perform the analysis from an Azure virtual machine.
Today is a freaking cool day. Why do you ask? Because today I am writing an article on how to use two of the coolest freaking big data/data science tools out there together to do epic shit! Lets start with HBase. HBase is a way to have a big data solution with query performance at an interactive level. So many folks are starting to just dump data into HBase. In the project teddy solution, we are dumping tweets, dialogue and dialogue annotations to power our open domain conversational api. There really is no other way that is easy to use for us to do this.
The second part of project teddy is to predict based on an incoming conversational component, what sort of response the speaker is attempting to illicit from the teddy bear. If we power our teddy bear with predictive analytics and big data, this would be perfect. What better platform to do this quickly and easily than AzureML?
As Azure becomes more and more popular and I encounter more startups, I find myself doing this tutorial all the time and explaining it. Therefor, I have decided to write a blog article with pictures, to make my life (and yours) easier.
What is BizSpark
BizSpark is the best thing since sliced bread for a startup. It is literally every development tool and license that Microsoft has to offer for free for commercial purposes for 3 years. Not only that, but you get $150/month (as of this writing) in Azure for 3 years as well. As if it couldn’t get any better, you get access to reduced pricing on various products from Microsoft Partners. Microsoft being such a giant of a company, there are a TON of partners you get special pricing from. BizSpark also includes product licensing such as just your simple windows licenses, or visual studio licenses, and even SQL Server licenses. Its everything! Usually at this point I get the question, so what’s the catch? There is no catch! Microsoft wants you to use their tools and be successful with their tools so that when you become a giant company, you are using their tools and not a competitors. Therefor Microsoft gives these tools to high potential startups for free! If you think you qualify, apply or come to an event I attend (usually found on the events tab). You can also ping me on twitter @DavidCrook1988.
Many folks may know that the South Florida Evangelism team is undertaking a task that many think is impossible. Well, in that statement all I hear is “there is still a chance!” The end goal is to create a teddy bear that can have a conversation about anything. So step one is to collect as much dialogue as possible from as many sources as possible and annotate them. What better place to power an association engine for word and phrase relevance than something that forces you down to 140 characters to get your message across.
So as any normal developer I decided to start by looking for samples already out there. MSDN has a great starter for writing tweets and doing sentiment analysis with HBase and C#. The only issue with the sample is, that it is very poorly written and difficult to understand with no separation of concerns. So I want to go through simplifying the solution and separating a few concerns out.
If you have ever done mapping applications, you may have encountered needing to do this. It takes a lot of looking around the internet to finally find the right equation etc. For our application, we need to do this for google maps, as it does not take a latitude/longitude combination like bing maps. If you choose to support ONLY bing maps, your job is easy, as here is the format: http://www.bing.com/maps/default.aspx?q=LATITUDE%2c+LONGITUDE (include negative signs if necessary). However Google maps requires more work (UGH!) https://www.google.com/maps/place/LATITUDE <directional> LONGITUDE <directional>, ZOOM (with various encoded separators).
This article goes through the code that converts latitude longitude like you will pull from a phone’s gps into the DMS format needed by google maps. Again, you don’t even need to bother with this conversion if you choose to use bing maps, it is simply as stated above.
So I had a life changing event this past Sunday at 8:55am 5/24/2015. My first child was born! Both child and wife are healthy and happy. Everything is good in life. Like many couples though, my wife and I struggled to find the right name for our child. We didn’t want something too common, or was an old person name, or so rare and funky that nobody could spell it. We also realized we just had a general lack in knowing what names were out there. So after much debate and discussion over what to name her, I started doing a bit of an analysis using some census data. I want to thank Jamie Dixon for providing the data that he found for use in his Dinner Nerds article. The data itself can be found here. This article will discuss the code used to go through all of the data and provide insights into child names.
Welcome to my blog’s version of the #IDevThis project that will be coming out. This full series will be made available on instructables and channel9 once it has been completed as a full unit.
Pi for Brains Robot (aka Danger-Bot)
These days it seems like I’m always dealing with a robot, they are on the phone, in my car, at the grocery store, just about everywhere. It also seems like every single one of them had Pie for brains, because it just couldn’t do what I told it to. This project is going to build a robot that ACTUALLY has PI for brains! Get out your breadboards, your Raspberry Pis and follow along. We will use a high performance cross platform robotics framework, Cylon, Raspberry Pi, and an Arduino Robotics Kit to build this internet connected robot.