Embedding Computational Intelligence in Pervasive Spaces

This paper presents how to embed AI mechanisms in pervasive spaces to produce more intelligent, adaptive, and convenient environments. The paper also concentrated on computational intelligence in particular, fuzzy systems and neural networks because this emerging AI domain covers most of the requirements for this task.     

Cross-Layer Packetization and Retransmission Strategies for Delay-Sensitive Wireless Multimedia Transmission

Existing wireless networks provide dynamically varying resources with only limited support for the quality of service required by the bandwidth-intense, loss-tolerant and delay-sensitive multimedia applications. This variability of resources does not significantly impact delay insensitive data transmission (e.g., file transfers), but has considerable consequences for multimedia applications. Recently, the research focus has been to adapt existing algorithms and protocols at the lower layers of the protocol stack to better support multimedia transmission applications and conversely, to modify application layer solutions to cope with the varying wireless networks resources. In this paper, we show that significant improvements in wireless multimedia performance can be obtained by deploying a joint application-layer adaptive packetization and prioritized scheduling and MAC-layer retransmission strategy. We deploy a state-of-the-art wavelet coder for the compression of the video data that enables on-the-fly adaptation to changing channel conditions and inherent prioritization of the video bitstream. We pose the cross-layer problem as a distortion minimization given delay constraints and derive analytical solutions by modifying existing joint source-channel coding theory aimed at fulfilling rate, rather than delay, constraints. We also propose real-time algorithms that explicitly consider the available information about previously transmitted packets. The obtained results show significant improvements in terms of video quality as opposed to ad-hoc optimizations currently deployed, while the complexity associated with performing this optimization in real time, i.e., at transmission time, is limited     

Impact of dimension uncertainty and model calibration on sewer system assessment.

Assessments of sewer performance are usually based on a single computation of CSO (combined sewer overflow) volumes using a time series of rainfall as system loads. A shortcoming of this method is that uncertainties in knowledge of sewer system dimensions are not taken into account. Moreover, sewer models are rarely calibrated. This paper presents the impacts of database errors and model calibration on return periods of calculated CSO volumes. The impact of uncertainties is illustrated with two examples. Variability of calculated CSO volumes is estimated using Monte Carlo simulations. The results show that calculated CSO volumes vary considerably due to database errors, especially uncertain dimensions of the catchment area. Furthermore, event-based calibration of a sewer model does not result in more reliable predictions because the calibrated parameters have low portability. However, it enables removal of database errors harmonising model predictions and 'reality'.