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M+M Middleware


Recently one of our works on middleware was published. Below is the link for you to access the published paper.

http://dl.acm.org/citation.cfm?id=2948942

Apart from the hardware, we at H+ have spent significant time with both SDK and Milddleware solutions that will allow developers and designers to participate in app creation efficiently. We believe you will enjoy working with these software tools and will be able to create wide range of applications that integrates 3rd party sensors very easily as well.

First let us explain about the middleware project, which was carried out in collaboration with SIAT, SFU at Surrey campus. The project was funded by Canarie organization and together with right resources we were able to work on software tool that allowed effective ways of networking with devices.

Primarily in the creative world, we are aware of OSC/MIDI solutions that are primarily point to point connections, for which there isn’t any centralized monitoring system which can give a user about incoming and outgoing connections at any given time. This makes it a bit harder to program complex systems which have many to many connections and every time the code needs to be changed which can further affect the entire system.

As a result, a middleware solution was needed that primarily is in the form of centralized monitoring system for all the connections. In the header image above is the concept for the middleware and all the peripherals that it supports.

Vision,

m+m: movement + meaning is developing a software framework that, broadly speaking, enables researchers to construct meaningful semantic models of movement data. The acquisition, processing, and rendering of movement data can be local or distributed, real-time or off-line. Examples of systems that can be built with m+m as the internal communication middleware include those for the semantic interpretation of human movement data, machine-learning models for movement recognition and movement analytics, the representation of semantic properties of movement data in virtual characters, and the mapping of movement data as a controller for online navigation, collaboration, distributed performance.

Key features of the m+m middleware are small footprint in terms of computational resources, portability between different platforms, and high performance regarding low latency and high bandwidth. We also wanted to provide a dedicated output service with Holus, so that one can easily connect any sensor. There are a number of platforms supported as well, however a dedicated Unity & Unreal Engine SDK for Holus will be supported to start with.

The basis of middleware is already open sourced and can be downloaded from mplusm.ca for which we have created a dedicated website.

Let us know your feedback on the middleware and how you plan to use it. If there any particular feature request, you can put them on the mplusm.ca forum and we will have a look at it. Furthermore, we are also going to have our dedicated forum on hplustech.com to further facilitate your requests.

- Dhruv Adhia, CTO

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