Towards compatible and integrated airport and regional land use planning in Australia

Over the past decade privatised capital city airports in Australia have planned developed a range of non aviation commercial and retail land uses on airport land. Many surrounding municipalities consider this development in conflict with existing regional land use planning. Conversely airport operators are alarmed at continued urban consolidation and encroachment of incompatible regional development. Land use planning within and surrounding Australian capital city airports does not support compatible and integrated land use. It is currently a fragmented process due to: 1) current legislative and policy frameworks; 2) competing stakeholder priorities and interests; and 3) inadequate coordination and disjointed decision-making. This paper will examine privatised Australian airport development and consider three case studies to detail the context of airport and regional land use planning. A series of stakeholder workshops have served to inform the procedural dynamics and relationships between airport and regional decision-making. This exploratory research will assist in informing the knowledge gaps between aviation, airport development and broader urban land use policy. This paper will provide recommendations to enhance approaches to land use planning for airports and adjacent metropolitan regions in Australia and overseas.

An accurate and reliable stereo matching algorithm incorporating the rank constraint

A fundamental problem faced by stereo vision algorithms is that of determining correspondences between two images which comprise a stereo pair. This paper presents work towards the development of a new matching algorithm, based on the rank transform. This algorithm makes use of both area-based and edge-based information, and is therefore referred to as a hybrid algorithm. In addition, this algorithm uses a number of matching constraints,including the novel rank constraint. Results obtained using a number of test pairs show that the matching algorithm is capable of removing a significant proportion of invalid matches. The accuracy of matching in the vicinity of edges is also improved.

Users segmentations for recommendation

Traditional recommendation methods provide recommendations equally to all users. In this paper, a segmentation method using the Gaussian Mixture Model (GMM) is proposed to customize users’ needs in order to offer a specific recommendation strategy to each segment. Experiment is conducted using a live online dating network data.

A semi-supervised graph-based algorithm for detecting outliers in online-social-networks

In this paper, we propose a semi-supervised approach of anomaly detection in Online Social Networks. The social network is modeled as a graph and its features are extracted to detect anomaly. A clustering algorithm is then used to group users based on these features and fuzzy logic is applied to assign degree of anomalous behavior to the users of these clusters. Empirical analysis shows effectiveness of this method.