DeviceHive Releases Version 2.0

DeviceHive Releases Version 2.0

We are proud to announce DeviceHive 2.0: faster, friendlier, more functional IoT Data Platform with a rich IoT Gateway framework. Get in touch with us if you want to learn more. Here are some of the key features included in this release.
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IoT: Authentication Basics in DeviceHive

Hello to lovers of the IoT and M2M things. My name is Artyom Sorokin and I'm a software engineer who has gained great experience working and developing IoT projects with the help of DeviceHive! This is a series of posts where I am going to show you the Authentication and Authorization models which are available in DeviceHive and how to use them. In this tutorial we will get in touch with the basic auth approaches implemented in DH. If you are just looking for a complete list of approaches without examples and the long description, just proceed to the "Summary" section at the bottom of this post.
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DataArt on the Leading Edge of Food Recognition

DataArt on the Leading Edge of Food Recognition

The DataArt Orange initiative spent lots of development efforts for its food recognition R&D project. And finally it feels that the market is ready for the technology. Last week Google announced at the Rework Deep Learning Summit an artificial intelligence project to calculate the calories in pictures of food you have taken. According to The Guardian, “the prospective tool called Im2Calories, aims to identify food snapped and work out the calorie content”. There is not much information about the project and what algorithms are available at the moment, but what is available indicates that Im2Calories will utilize a similar approach used by DataArt’s Computer Vision Competence Centre researchers in their Eat’n’Click project.
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Thinking About the Future of Wearables Today

Igor Kozhurenko, VP of Research & Development at DataArt, was asked to comment on the Apple Watch release and the future of wearables for the Sprint Business blog.
"The launch of Apple Watch has meant that wearable technologies – and smart watches in particular – are getting a lot of attention. Business Insider estimates that the smart watch will be the leading product category on the wearable market and will account for 59% of total wearable device shipments this year, expanding to just over 70% of shipments by 2019. Paired with a smartphone, smart watches can offer rich functionality across a number of verticals like healthcare, travel, the smart home, IoT and capital markets. Their future, however, is tied to two factors:
  • The reliability of the hardware;
  • Stellar services that could empower the people wearing wearables.
Here’s what needs to be understood. Smart watches are not another type of a mobile device. They’re a new generation of technology, and call for a whole new approach to application design. For your app to thrive, you need to account for three basic realities:
  • The screen has a limited amount of space;
  • The user flow is entirely different from those of mobile applications;
  • Users expect a personalized experience.
DataArt design and R&D teams took all of the above into consideration when developing the approach for developing apps for wearables. We realized that whatever experiences the app is expected to deliver they should be achieved in fewer than three taps, which requires prioritizing notifications and assuring the app doesn’t become “spammy”, driving users away."
View original article or download PDF
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Smart watch will win the market, and here’s why

Smart watch will win the market, and here’s why

The launch of Apple Watch has meant that wearable technologies – and smart watches in particular – are getting a lot of attention. Just think about this; according to Juniper research, over 70M fitness wearable devices are expected to be in use worldwide by 2018, up from 19M in 2014. On top of that, Business Insider estimates that the smart watch will be the leading product category on the wearable market and will account for 59% of total wearable device shipments this year, expanding to just over 70% of shipments by 2019.
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DeviceHive at Microsoft Build 2015

DeviceHive at Microsoft Build 2015

We are glad to announce that the DeviceHive team together with Canonical/Ubuntu will support showcase IoT solutions using Ubuntu Snappy Core and Microsoft Azure at Microsoft Build 2015. If you would like to chat, please get in touch with us to schedule a meeting. Tickets for Microsoft’s annual developers conference were sold out in just an hour. It will take place in San Francisco on April 29th through May 1st. It’s really exciting to see where the company’s IoT vision will lead us. Microsoft CEO Satya Nadella said that “the new product, “Azure IoT Suite”, will combine business intelligence capabilities (Power BI) using real-time data (Azure Stream Analytics) with Azure Machine Learning capabilities”. Also he announced that Azure IoT Suite will be available as a preview later this year. While few details about the suite were provided, it will be designed to address various IoT scenarios “such as remote monitoring, asset management and predictive maintenance.”
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ResearchKit for Developers

The heading on Apple’s ResearchKit page says, ‘Nothing is more important than our health’. With that motto in mind, a new framework was created. The iPhone has now got a new feature, it can be used as a medical research device to help doctors and patients. The ResearchKit framework will be officially available in April and Apple has already published 5 applications (for the U.S. only) supporting it.
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IoT: instant and scalable integration with DeviceHive and Juju

IoT: instant and scalable integration with DeviceHive and Juju

DataArt, a technology consulting firm,  has joined the Charm Partner Programme. Canonical’s Charm Partner Programme helps solution providers make best use of Canonical’s cloud orchestration tool, Juju; enabling instant integration, scaling at the click of a button, simple to share blueprint deployments and an easy way to deliver solutions in minutes. DataArt’s product DeviceHive is an opensource solution enabling easy connectivity between devices, making any connected device part of the Internet of Things (IoT).  It provides the communication layer, control software and multi-platform libraries to bootstrap development of smart energy, home automation, remote sensing, telemetry, remote control and monitoring software, and much more.
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How To: Use GoogleFit API

How To: Use GoogleFit API

Today we’ll talk about the basics of the GoogleFit API for the Android platform and will try to put this knowledge into practice. We’ll create a project and read fitness data from the available sensors, store them in a GoogleFit cloud and later read the history. Also we will overview the possibilities to apply this experience to real projects.
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How To: Fitbit – New Development Opportunities

The world is excited about the new Fitbit wearable devices. So is DataArt's Wearables Competence Center. We decided to play with some of them to find out about the new development opportunities. This article is dedicated to Fitbit.
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DeviceHive 1.3.0 – the Open Source IoT/M2M Platform Done Right

DeviceHive 1.3.0 – the Open Source IoT/M2M Platform Done Right

The DataArt IoT / M2M practice is happy to finally present the new release version of the open source IoT / M2M communication platform DeviceHive 1.3.0. The framework was developed to allow users to concentrate more power in innovation & focus on how the machines & gadgets will communicate, instead of the type of data they transmit and what the overall end-user experience could be. This fall the new big things with extended security support and scalability are coming.

The DeviceHive team has released the multipurpose JavaScript library in the new 1.3.0 version. Now, large and complex applications with Client-Server-Device architecture can be easily coded with 10 lines, as the new DeviceHive JavaScript library is available on the device side as well as the client side. For this purpose, the new DHDevice component is used. It can be downloaded from this module: devicehive.device.js. In order to simplify the installation and packages management, the client’s and the device’s JavaScript libraries were integrated with Bower. A second after entering “bower install DeviceHive”, you will be able to start writing your JavaScript-DeviceHive application! The JavaScript library development is still a dynamic process. In our next patch release we're going to expand the horizons further by adding full support for node.js environment.

You can view more details here.

The new DeviceHive release has the integration with Docker – so version 1.3.0 is now cloud-compatible! Now all deployments to the cloud are easy to make. In previous versions of DeviceHive, the issue of server deployment was resolved by a set of complex scripts that required a very specific environment - not a handy thing.

DeviceHive’s new infrastructure deployment approach provides quick and easy server deployment. DeviceHive Java server is now integrated with Docker, which makes deployments to cloud environments extremely easy. This platform allows fast deployment of the new DeviceHive servers in any Docker-compatible environment, and migration of the configured DeviceHive servers to other hardware or virtual platforms.

The short instruction of how to quick start using Docker is at the DeviceHive site.

To fully enjoy the new DeviceHive 1.3.0, please, use the links below.

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DataArt Orange to integrate its food recognition engine with Apple HealthKit and Voice Recognition

Eat’n’Click is an application developed by DataArt that helps people track the nutritional content of foods they consume. The app automatically tracks calorie information by scanning photographs of food.

Its prototype is available on the App Store and can recognize a number of fruits with more than an 85% success rate. Recently the application was demonstrated by the DataArt team during the Health 2.0 Europe 2014 event in London and was highly appreciated by its participants.

Following industry trends and the high attention Apple HealthKit is receiving, DataArt’s Research Lab decided to move forward by automating the tracking of users’ nutrition habits. DataArt is planning to build a PoC prototype to integrate with Apple HealthKit. Also, we plan to implement a voice recognition engine with it. The aim of DataArt’s Orange R&D initiative is to reduce the gap between humanity and computers. And we strongly believe that this step will be highly effective.

FoodR Slim to be integrated with Apple HealthKit and Voice Recognition
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DataArt Computer Vision Team To Develop Object Tracking PoC

Responding to a request from a potential customer, DataArt’s R&D department created a prototype application for object recognition and tracking within a video. The customer’s idea for the product was that an object, selected manually in a video for the first time, is then tracked automatically throughout the footage, with the object coordinates retrieved and stored as the video progresses. DataArt’s computer vision scientists and engineers timely conducted a feasibility study, which appeared to be positive, and created a prototype object tracking application. The application allows loading a video, pointing at an object at a specific frame (or again later if the automatic tracking fails), and runs an object tracking analysis over the loaded file. The object is located in the following frames, and the location of the object is stored along with the video as a key-value file, where the calculated coordinates of the object correspond to the current playback time. A commercial playback application could, using this information, then place an ad over the video at these specific coordinates during the playback, thus allowing for dynamic context advertising. film-5  
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DataArt ORANGE at Health 2.0 Europe 2014

  DataArt’s Healthcare & Life Sciences Practice has more than 10 years of medical software development experience  and is willing to present its achievements. November 10-12 this year, DataArt is exhibiting at Health 2.0 Europe conference in London. It is the second time Health 2.0 takes place in London bringing together the latest in health technology and inspiring thought-provoking discussions. Last year’s conference proved to be a great success, and this one is bound to be a new exciting edition. DataArt believes that an event of this scale helps the industry to move forward, and we can’t wait to discuss hot industry topics at the event. Among other things, we are proud to demonstrate how our RnD project ORANGE can give a great boost to your healthcare & life science business. Please come visit us at our booth at Health 2.0.
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DataArt Embedded Lab announces support for another three popular core boards for DeviceHive educational hardware set

DataArt Embedded Lab, which started with an Arduino core for its first IoT hardware education platform a year ago, has added another three popular boards to the roster of cores it has platforms for: The Intel Galileo, BeagleBoard’s BeagleBone Black, and a very promising ARM + AVR symbiotic product, the Arduino YUN. All core boards are connected to the unified platform’s motherboard periphery via the wire crossing (breakout) layer, thus allowing for the mapping of alike ports across the different platforms. Along with the DeviceHive’s practice of using a hardware-independent RTOS for the lightweight boards (those that are not powerful enough to run a Unix-family OS or establish an Ethernet or Wi-Fi connection), this facilitates fast and easy user code migration across the boards.  More hardware-rich platforms that have their own Unix branches also have the advantage of cross-platform user code. This is particularly relevant in education – which is DataArt's target usage of the platforms. microcontrollers_arduino_galileo_beagleboard The five current DeviceHive boards cover all popular DIY boards – not only by software, but by the evaluation hardware they support as well. This shows the growing potential of the Lab in hardware development, and also the software team's  growing ability to tailor the code and build-up a fine grained technical expertise, having the variety of the platforms on-hand.
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A Digital Pen in the IoT System with the DeviceHive Platform

Business professionals and consumers are discovering the benefits of wireless digital pens. By writing on special digital paper or directly on select computer screens, digital pen users can convert their sketches, diagrams, and any other type of handwritten communication into electronic files instantaneously. These files can integrate across devices and applications. Users can synchronize their notes with electronic calendars, provide their electronic signatures on contracts in Adobe PDF format, and create visual overlays onto web content. They can also convert handwritten notes into text, allowing mobile users to create and share “mobile notes”. The technology decreases paper usage and the need for manual electronic conversion tasks, and adds some interesting new capabilities for electronic communication.
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Fruit recognition – some improvement

The quality of visual fruit recognition PoC was improved by adding texture and RGB color space features information to the feature vector. Same as in our previous experiment, we took the neural network as a classifier. The network learns with error of 0.155. The results are given below: fruit recognition In general, the quality of recognition was increased in 0.112%.
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How To: Integrate Your Heart Rate App with HealthKit API

At the Apple Worldwide Developer Conference earlier this month Apple introduced an app simply called “Health”that promises to become an all-embracing hub for healthcare and fitness applications and wearable devices. It will allow you to store all of your health data and vital signs in one place including your heart rate, blood pressure, blood sugar, sleep patterns, consumed and burnt calories and more. It will also give you control over all of your health apps through one interface. The news inspired our team to integrate our health apps with the HealthKit API to help bring the world one step closer to integrated health data. We started with our Heartbeat Rate application that processes the video stream from your iPhone/ iPod camera to measure heart rate. It analyzes the bluecomponent in the video stream providing reliable results, thus no physical contact is required. (Read more about Heartbeat Rate application.) Here is the recap of how our Heartbeat rate app was integrated with HealthKit API.
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DataArt Orange integrates with Apple HealthKit

Apple HealthKit announcement two days ago was exciting news for all mHealth community and millions of fitness apps users throughout the world. DataArt R&D team recognizes the potential of iOS 8 and all fitness data integration. We already started looking into HealthKit API to integrate our R&D projects with the new hub and ultimately to help establish wearable technology interoperability and health and fitness data interchange.
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DataArt Computer Vision Lab Released Football Clubs Recognizer

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DataArt Research Lab has released its application for football fans that visually identifies football clubs. Football Clubs Recognizer is available on the Apple App Store. In preparation for the 2014 Football World Cup, DataArt Computer Vision experts developed the first version of an application that provides full information about football teams including their place in the standings, roster, list of recent and upcoming matches, information about players, etc. To get information regarding a certain team just point the phone camera at a team logo wherever it may be displayed. The application can successfully recognize logos from screens, newspapers, etc.
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Fruit Recognition – Continuing Research

Fruit Recognition – Continuing Research

DataArt Research Lab continues to publish the results of fruit recognition using the Color Distance method. The Color Distance method showed good result as an engine for fruit recognition. Just for testing purposes, we took 15 classes of fruits (10 ‘ideal’ samples for each class) and a simple classifier of Euclidian distance. But such an approach has serious disadvantages:
  • in everyday life we do not deal with ideal pictures: the photos may contain different distortions, irregular brightness  etc.;
  • the classifier of Euclidian distance cannot provide us with real time work, particularly if we have a lot of classes and etalons.
So our next step is to use a more complicated real time working classifier and increase the sample set with new ‘real’ images.
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Trends in Media: Analysis with Big Data

Trends in Media: Analysis with Big Data

DataArt’s Big Data Competence Center announces the launch of a new beta computer application. The app analyses U.S. and U.K. media news flow and converts it into easy-to-understand charts and infographics.
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DataArt Announces ORANGE – the Missing Piece in Nutrition Tracking

New R&D initiative provides food recognition technology to calculate calorie intake NEW YORK – March 27, 2014DataArt, a leading custom software development company that builds advanced solutions for select industries, today announced the first results of DataArt ORANGE, a series of research and development projects that aim to automate the tracking of users’ nutrition habits. The DataArt ORANGE program automatically tracks calorie information by scanning photographs of food. DataArt ORANGE technology can currently recognize over 100 foods with an 85% success rate.
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DataArt Research Lab publishes Color distance method testing results

DataArt Research Lab publishes Color distance method testing results

The color distance method has been tested using 15 fruit types, each type having 10 photos. The following recognition score calculation algorithm has been used:

  • Three guesses (ordered best to worst) are considered.
  • A correct guess placed first obtains 10 points, placed second – 5, placed third has 1.
  • For all tests for a particular fruit type the obtained points are summed up and divided by the absolute maximum result 10 * N, where N – the number of tests for the class, thus yielding the classification quality for the type.
  • The overall classification quality for the whole fruit set, the individual classification qualities for every fruit type are averaged.
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DataArt Research Lab to experiment on finding and proofing feature extraction methods suitable for food recognition tasks

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Meals named the same rarely look similar. This is not only due to different people cook differently – in the computer vision sense, meals are combinations of areas (spots) with different color, texture, shape each. This makes typical image recognition principles less suitable for food image recognition, as we cannot rely on either form or relative position of the image parts. Typically, if local peculiarities of objects being detected cannot be caught, integration feature extraction methods take over differential one – e.g. in our current food image classification engine we mostly rely on combined histogram and texture parameters for the whole image. This approach shows relatively good results unless the meal we’re trying to classify appears to have no noticeable texture features.
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DataArt Has Started an R&D Project in Remote Human Pulse Detection, Based on Digital Signal Processing Principles

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Heartbeat Rate app allows to measure the pulse using video processing algorithm
Inspired by recent publication on a video processing algorithm, which is able to detect and magnify subtle periodic changes in color in the series of video frames, DataArt has started experiments on adding heartbeat measurement possibility to their Microsoft Kinect-based healthcare solution. The principle of detection is based on the fact that the human skin becomes more red when the blood pressure is at its maximum (systolic pressure), and less red when the pressure is at its minimum (diastolic pressure). For people not having arrhythmia, these changes are periodic, and therefore, its’ frequency can be caught and measured using spectrum analysis principles.
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DataArt is Building Face Recognition Application for iOS

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To broaden its professional horizons and get involved into something new, DataArt decided to dive into computer vision area, and to be more accurate, face recognition techniques. Our computer vision group created face recognition app that has access to DataArt employees’ database and could recognize them.
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Latest Audio Steganography Techniques on Practice

Latest Audio Steganography Techniques on Practice

Improvements in processor performance of desktop PCs and handheld devices has driven technology to melt chunks of digital information into media streams (an audio file, a TV, radio, Internet broadcast, or an authorized digital content distribution network). The physical principles and mathematics for such techniques were developed long ago; the technical progress is the trigger of making audio steganography a reality. What was considered a ‘spy’ or a secret lab technology ten years ago, is now available to public as a turnkey-quality SDK, or a ready app. This will create more demand and new fields of application.

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