Planning Through Dialogue for Rural Development: The European Citizens’ Panel Initiative

This paper presents a narrative of the operation of the European Citizens’ Panel that reported in 2007 on the future roles of rural areas. This dialogue was located within a wider and recent engagement by the EU with its citizens following rejection of the EU Constitutional Treaty. The paper draws attention to the contemporary rural development challenges in Europe that were debated by eight regional panels as a prelude to a wider European deliberation. The working method of the European Citizens’ Panel is outlined and critical commentary is provided on the interaction between planning through dialogue, EU citizenship renewal, and the shaping of bottom-up development trajectories.

Housing Rural Communities: Connecting Rural Dwellings to Rural Development in Ireland

This paper explores the interaction between rural development policy and spatial planning policies for rural housing within the context of the island of Ireland. It draws on research commissioned by the Northern Ireland Housing Executive for a high level review of its rural housing policy. The paper highlights issues of wider relevance comprising a strained relationship between rural communities and rural planning, and argues for the adoption of cultural, environmental and community values within the rural planning policy arena.

Cost function adaptation: a stochastic gradient algorithm for data echo cancellation

A family of stochastic gradient algorithms and their behaviour in the data echo cancellation work platform are presented. The cost function adaptation algorithms use an error exponent update strategy based on an absolute error mapping, which is updated at every iteration. The quadratic and nonquadratic cost functions are special cases of the new family. Several possible realisations are introduced using these approaches. The noisy error problem is discussed and the digital recursive filter estimator is proposed. The simulation outcomes confirm the effectiveness of the proposed family of algorithms.