Robust and adaptive diffusion-based classification in distributed networks

Distributed adaptive signal processing and communication networking are rapidly advancing research areas which enable new and powerful signal processing tasks, e.g., distributed speech enhancement in adverse environments. An emerging new paradigm is that of multiple devices cooperating in multiple tasks (MDMT). This is different from the classical wireless sensor network (WSN) setup, in which multiple devices perform one single joint task. A crucial first step in order to achieve a benefit, e.g., a better node-specific audio signal enhancement, is the common unique labeling of all relevant sources that are observed by the network. This challenging research question can be addressed by designing adaptive data clustering and classification rules based on a set of noisy unlabeled sensor observations. In this paper, two robust and adaptive distributed hybrid classification algorithms are introduced. They consist of a local clustering phase that uses a small part of the data with a subsequent, fully distributed on-line classification phase. The classification is performed by means of distance-based similarity measures. In order to deal with the presence of outliers, the distances are estimated robustly. An extensive simulation-based performance analysis is provided for the proposed algorithms. The distributed hybrid classification approaches are compared to a benchmark algorithm where the error rates are evaluated in dependence of different WSN parameters. Communication cost and computation time are compared for all algorithms under test. Since both proposed approaches use robust estimators, they are, to a certain degree, insensitive to outliers. Furthermore, they are designed in a way that they are applicable to on-line classification problems

Ambient voice control for a personal activity and household assistant

Technologies for ambient assisted living (AAL) are used to increase the quality of life of older or impaired persons. This contribution discusses the utilization of automatic speech recognition (ASR) as a natural interface for control of assistive technologies in everyday life situations. We focus on the use of hands-free systems, the technical challenges for the ASR software caused by this and the benefits for older persons. Moreover, state-of-the-art approaches for improving robustness of ASR systems are presented, discussed and demonstrated by an ASR experiment

The effects of 1 1.5 and 2 degrees of global warming on Africa in the CORDEX ensemble

There is a general lack of information about the potential effects of 1.5, 2 or more degrees of global warming on the regional climates within Africa, and most studies that address this use data from coarse resolution global models. Using a large ensemble of CORDEX Africa simulations, we present a pan-African overview of the effects of 1.5 and 2 ◦C global warming levels (GWLs) on the African climate. The CORDEX simulations, consistent with their driving global models, show a robust regional warming exceeding the mean global one over most of Africa. The highest increase in annual mean temperature is found over the subtropics and the smallest one over many coastal regions. Projected changes in annual mean precipitation have a tendency to wetter conditions in some parts of Africa (e.g. central/eastern Sahel and eastern Africa) at both GWLs, but models’ agreement on the sign of change is low. In contrast to mean precipitation, there is a consistent increase in daily precipitation intensity of wet days over a large fraction of tropical Africa emerging already at 1.5 ◦C GWL and strengthening at 2 ◦C. A consistent difference between 2 ◦C and 1.5◦Cwarmings is also found for projected changes in annual mean temperature and daily precipitation intensity. Our study indicates that a 0.5 ◦C furtherwarming (from1.5◦C–2 ◦C) can indeed produce a robust change in some aspects of the African climate and its extremes.JRC.E.1-Disaster Risk Managemen