For the third edition of my Exploratory Data Analysis (EDA) series, I shall be exploring the human heart, computationally. The inspiration for this topic came from a quote in the recently released film The Man Who Knew Infinity. This post looks at the heart from the perspectives of semantics, languages and translations, and anatomy.
For the second edition of my exploratory data analysis series, I shall be analysing data on trees in the city Belfast. The dataset is supplied and made accessible by Open Data NI. In this article I will be showing analysis I have done on the dataset, ranging from clustering of species to nonparametric statistical distributions.
Over the course of the summer exams period, I gathered as many datapoints as possible and played with the dataset. The aim was to find something which can be benefial to every Queen's student, in or out of exam season. In this post I will discuss what I have done so far with the ever-growing dataset, and how I'm using the Wolfram Language and other technologies such as Wolfram Data Drop and Wolfram|Alpha to store and analyse it. I hope you find this both interesting and useful.
Quite a while ago, I wrote about an application I made using api.ai, a conversational UX platform with a powerful voice recognition engine. Api.ai allows you to control your application using voice recognition, amongst other services it offers. I've been looking at how tech like this can be integrated into IoT applications. In this post, I wiill discuss an example I recently explored: turining lights on and off, using voice command.
I recently got the Intel Edison kit for Arduino and I've had so much fun playing around with it. In this post, you'll learn how to stream in data from a temperature sensor and visualise it in Mathematica.
Airports & Heliports is a simple web application powered by the Wolfram Cloud that visualises on a map, the number and locations of airports and heliports in any given city. You enter the name of the city you want, a map is generated in the Wolfram Cloud and returned in PDF format which can then be saved.
api.ai is a tool which offers voice recognition, natural language processing and text-to-speech and allows users to integrate speech interfaces into their products. Six languages and four Software Development Kits (including Android and iOS) are currently available. A free account allows up to 3,000 monthly queries and it should be straightforward to implement, after reading the api documentation. I tried to create a little interface which uses this api along with previously discussed ReKognition to recognise faces using voice commands, in Mathematica. Initially, the aim was to try-out api.ai but it soon became too intriguing to leave it there.
The Travelling Salesman Problem (TSP) is a graph theory problem which requires the most efficient, i.e. shortest, closed loop path through which a salesman can travel to each of n cities, exactly once . Such a loop is called a Hamiltonian cycle  and there is no known general solution to the TSP. Practical applications to the solution of the TSP include the scheduling of tasks on a computer and ordering of genome features . Inspired by the last example on this page, I tried projecting the shortest path through every country on the planet and visualising it on a map.
Near the end of last year, I stumbled upon Orbeus -- a computer vision company that offers facial, object and scene recognition technology . This can be implemented using Orbeus' cloud-based ReKognition API. A ReKognition free Starter account allows one up to 5000 API monthly calls, with a limit of a single call per second and a tiny extra charge, if the maximum is exceeded. Using this API in Mathematica is pretty easy. Likewise, visualising the results. I have always been interested in this sort of tech and so I signed up for a free account to check it out.