Complexity Engineering

CAHMS Applications

The CAHMS – Complexity And Health Monitoring System – applications fall into two main categories:

CAT 1: Analysis of real-time data from sensors located on a single mobile platform, such as aircraft, a submarine, a tank or a drone.

CAT 2: Analysis of real-time data from sensors located on multiple objects, such as swarms of drones or soldiers engaged in a battle operation.

In both cases, the goal is to:

  • Identify sudden increases in complexity, which point to potential anomalies
  • Pinpoint the sources of complexity increase
  • Understand the structure of component interactions (Complexity Map)
  • Identify key players (drivers of complexity and fragility)
  • Rank channels or components (Complexity Profile)
  • Appreciate the dynamics and evolution of a given situation (Complexity Evolution)

The CAHMS can be mounted on modern mobile platforms as well as retrofitted on older equipment, provided streaming raw (digital) data is available. There is no data transmission – it all happens onboard, in real time – no Machine Learning, no fuss.

Both categories of CAHMS application are illustrated below

CAT 1: Single Mobile Platform

Click on images to enlarge.

CAT 2: Swarm

In the case of a (large) number of soldiers, complexity monitoring can help anticipate increases in stress and anxiety, as well as indicate breakdown of tactics and strategy.

Established originally in 2005 in the USA, Ontonix is a technology company headquartered in Como, Italy. The unusual technology and solutions developed by Ontonix focus on countering what most threatens safety, advanced products, critical infrastructures, or IT network security - the rapid growth of complexity. In 2007 the company received recognition by being selected as Gartner's Cool Vendor. What makes Ontonix different from all those companies and research centers who claim to manage complexity is that we have a complexity metric. This means that we MEASURE complexity. We detect anomalies in complex defense systems without using Machine Learning for one very good reason: our clients don’t have the luxury of multiple examples of failures necessary to teach software to recognize them. We identify anomalies without having seen them before. Sometimes, you must get it right the first and only time!

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