A Low Cost Controller Board for Teaching Robotics

This paper presents the Smarty Board; a new micro-controller board designed specifically for the robotics teaching needs of Australian schools. The primary motivation for this work was the lack of commercially available and cheap controller boards that would have all their components including interfaces on a single board. Having a single board simplifies the construction of programmable robots that can be used as platforms for teaching and learning robotics. Reducing the cost of the board as much as possible was one of the main design objectives. The target user groups for this device are the secondary and tertiary students, and hobbyists. Previous studies have shown that equipment cost is one of the major obstacles for teaching robotics in Australia. The new controller board was demonstrated at high-school seminars. In these demonstrations the new controller board was used for controlling two robots that we built. These robots are available as kits. Given the strong demand from high-school teachers, new kits will be developed for the next robotic Olympiad to be held in Australia in 2006.

Turbocharging Networks in the Public Sector

Networks have come to occupy a key position in the strategic armoury of the government, business and community sectors and now have impact on a broad array of policy and management arenas. An emphasis on relationships, trust and mutuality mean that networks function on a different operating logic to the conventional processes of government and business. It is therefore important that organizational members of networks are able to adopt the skills and culture necessary to operate successfully under these distinctive kinds of arrangements. Because networks function from a different operational logic to traditional bureaucracies, public sector organizations may experience difficulties in adapting to networked arrangements. Networks are formed to address a variety of social problems or meet capability gaps within organizations. As such they are often under pressure to quickly produce measurable outcomes and need to form rapidly and come to full operation quickly. This paper presents a theoretical exploration of how diverse types of networks are required for different management and policy situations and draws on a set of public sector case studies to understand/demonstrate how these various types of networked arrangements may be ‘turbo-charged’ so that they more quickly adopt the characteristics necessary to deliver required outcomes.

Integrating science through Bayesian Belief Networks : case study of Lyngbya in Moreton Bay

Bayesian Belief Networks (BBNs) are emerging as valuable tools for investigating complex ecological problems. In a BBN, the important variables in a problem are identified and causal relationships are represented graphically. Underpinning this is the probabilistic framework in which variables can take on a finite range of mutually exclusive states. Associated with each variable is a conditional probability table (CPT), showing the probability of a variable attaining each of its possible states conditioned on all possible combinations of it parents. Whilst the variables (nodes) are connected, the CPT attached to each node can be quantified independently. This allows each variable to be populated with the best data available, including expert opinion, simulation results or observed data. It also allows the information to be easily updated as better data become available ----- ----- This paper reports on the process of developing a BBN to better understand the initial rapid growth phase (initiation) of a marine cyanobacterium, Lyngbya majuscula, in Moreton Bay, Queensland. Anecdotal evidence suggests that Lyngbya blooms in this region have increased in severity and extent over the past decade. Lyngbya has been associated with acute dermatitis and a range of other health problems in humans. Blooms have been linked to ecosystem degradation and have also damaged commercial and recreational fisheries. However, the causes of blooms are as yet poorly understood.