Influence of Training Set Selection in Artificial Neural Network-Based Propagation Path Loss Predictions

This paper analyzes the use of artificial neural networks (ANNs) for predicting the received power/path loss in both outdoor and indoor links. The approach followed has been a combined use of ANNs and ray-tracing, the latter allowing the identification and parameterization of the so-called dominant path. A complete description of the process for creating and training an ANN-based model is presented with special emphasis on the training process. More specifically, we will be discussing various techniques to arrive at valid predictions focusing on an optimum selection of the training set. A quantitative analysis based on results from two narrowband measurement campaigns, one outdoors and the other indoors, is also presented.

Factor analysis based VTS discriminative adaptive training

Vector Taylor Series (VTS) model based compensation is a powerful approach for noise robust speech recognition. An important extension to this approach is VTS adaptive training (VAT), which allows canonical models to be estimated on diverse noise-degraded training data. These canonical model can be estimated using EM-based approaches, allowing simple extensions to discriminative VAT (DVAT). However to ensure a diagonal corrupted speech covariance matrix the Jacobian (loading matrix) relating the noise and clean speech is diagonalised. In this work an approach for yielding optimal diagonal loading matrices based on minimising the expected KL-divergence between the diagonal loading matrix and "correct" distributions is proposed. The performance of DVAT using the standard and optimal diagonalisation was evaluated on both in-car collected data and the Aurora4 task. © 2012 IEEE.

3D Hand Pose Estimation by Finding Appearance-Based Matches in a Large Database of Training Views

Ongoing work towards appearance-based 3D hand pose estimation from a single image is presented. A large database of synthetic hand views is generated using a 3D hand model and computer graphics. The views display different hand shapes as seen from arbitrary viewpoints. Each synthetic view is automatically labeled with parameters describing its hand shape and viewing parameters. Given an input image, the system retrieves the most similar database views, and uses the shape and viewing parameters of those views as candidate estimates for the parameters of the input image. Preliminary results are presented, in which appearance-based similarity is defined in terms of the chamfer distance between edge images.

Approved Social Work Training in Northern Ireland: Using Research to Examine Competence-Based Learning and Influence Policy Change

This article reports on how research activity helped describe and analyse ASW (Approved Social Worker) learning experience as well as acting as a catalyst for change and development in policy and practice in Northern Ireland. The paper contextualizes the study by outlining the legislation, the main features of the ASW role and the approach to ASW training in Northern Ireland, and by reviewing the literature on the efficacy and value of competence-based learning. While the findings do not provide conclusive evidence that a competence-based approach is inherently more effective than previous courses, they do indicate that candidates who were trained in this way were moderately more satisfied than those who had participated in non-competence based programmes. The research also highlights the importance of the interrelationship between training, practice experience and support in developing and sustaining competence. The paper concludes with a review of the recommendations arising from the study and an analysis of the developments in training and regulations relating to practice experience and re-approval of ASWs since publication of the research. The study is of contemporary interest given the proposed changes to the role of ASWs/Mental Health Officers in the context of the reviews of UK mental health law.