Abstract
The usage of Wireless Sensor Networks (WSNs) in industrial environments has been steadily increasing, due to the reduced deployment and maintenance costs, when compared to the use of wired networks for connecting single I/O points in industrial applications. The most promising WSN standard is the one defined by the IEEE 802.15.4 standard. However, it does not support the adequate mechanisms to deal with the unreliable nature of an industrial communication environment. In this paper, we propose an extension of the use classes available for this standard, together with the proposal of adequate message retransmission schemes, in order to increase the reliability of message exchanges in industrial environments. To demonstrate the effectiveness of the proposed schemes, we present a simulation assessment of both the proposed message retransmission schemes and the Guaranteed Time Slots (GTS) mechanism, as defined by the standard. © 2012 IEEE.

Abstract

Abstract
Wireless sensor networks composed of camera-enabled source nodes can provide visual information of an area of interest, potentially enriching monitoring applications. While few bytes can represent scalar data, even low-resolution still images may require thousand of bytes, turning data fragmentation into a relevant design issue. Different optimization approaches have been proposed in recent years to achieve energy saving in wireless image sensor networks. However, the impact of image fragmentation upon the adopted MAC technology has been neglected in most cases. In this work we investigate the effect of frame size on image transmissions over wireless sensor networks, linking the maximum frame size, the useful payload and the frame error rate effects. Additionally, we discuss different approaches for transmissions of DWT-based encoded images and the impact of inserting application-specific information into the frame header. We believe that our discussions can contribute to the advance of the design of wireless image sensor networks. © 2012 IEEE.

Abstract
In this work, a novel method for segmentation of Remote Sensing (RS) images based on the Darwinian Particle Swarm Optimization (DPSO) for determining the n-1 optimal n-level threshold on a given image is proposed. The efficiency of the proposed method is compared with the Particle Swarm Optimization (PSO) based segmentation method. Results show that DPSO-based image segmentation performs better than PSO-based method in a number of different measures.

Abstract

Abstract
Hand-geometry biometric recognition is normally based on the detection of five points that correspond to the fingertips and four points between them (valley points). Specific methods often have to be implemented during the acquisition stage to make the detection of those points easier. This study presents techniques that have been developed to overcome the difficulties and limitations of the current systems. Moreover, a hand-geometry based recognition system that has no constraints during image acquisition is presented. A methodology was developed based on the hand skeleton for the points on the fingertips and for the valley points it was based on the curvature of the hand contour. The principal difficulties were found during the segmentation step, which often fails if the fingers are not spread out. Once the points have been located, the necessary features for authentication were extracted. Classification algorithms were implemented at this stage. Those showing the best results presented a Genuine Acceptance Rate (GAR) of 76% and 8% for the False Acceptance Rate (FAR).