Computational Intelligence for Complex Structured Data

该项目旨在使用计算智能技术可靠地学习自适应的自然人类指向和手势，以控制伪3d显示器上的界面。元素之间相互联系的高度复杂的数据很难在屏幕上可视化。目前大多数工具都是在2D屏幕上使用点/点击/拖动操作的。捕捉适当人类行为的物理技术已经存在，但还没有对单个手势和动作的语法和语义的自适应学习，也没有理解所需的多手势信息融合，这可以显著提高效率，例如，在调查中对命名实体进行排序。所有这些都是大多数人利用生物神经网络自然完成的。

This project aims to use computational intelligence techniques to reliably learn adaptive natural human pointing and gestures to control an interface on a pseudo-3D display. Highly complex data with interconnections between elements is hard to visualise on screens. Most current tools are operated using point/click/drag on 2D screens. The physical technology to capture appropriate human behaviours exists already, but not the adaptive learning of the syntax and semantics of individual gestures and actions, nor the multi-gesture information fusion required for understanding, which could significantly enhance efficiency, for example, in sorting through named entities in an investigation. All of this is done naturally by most human beings, using biological neural networks.