Abstract

Abstract

Abstract
Techniques of time series analysis were used to examine historical records of the incidence of diphtheria, pertussis, and measles, and of deaths by measles in Portugal during the twentieth century. There are statistically significant seasonal and long-term oscillations in the incidence of these diseases. Seasonal oscillations appear to be in close association with the resumption of school classes in the fall in the case of diphtheria, but not in pertussis and measles. Long-term oscillations in pertussis (3.5-4 year period) and measles (3-year period), before vaccination, corroborate theoretical predictions about the dynamics of these diseases, whereas absence of long-term oscillations in diphtheria is probably due to the influential presence of carriers upon the dynamics of the disease. Mass vaccination strongly suppressed disease incidence, did not eliminate seasonal oscillations, and appeared to have acted to lengthen long-term periodicity in pertussis and measles.

Abstract

Abstract
Intelligent wheelchairs operating in dynamic environments need to sense its neighborhood and adapt the control signal, in real-time, to avoid collisions and protect the user. In this paper we propose a robust, real-time obstacle avoidance extension of the classic potential field methodology. Our algorithm is specially adapted to share the wheelchair's control with the user avoiding risky situations. This method relies on the idea of virtual forces, generated by the user command (attractive force) and by the objects detected on each ultrasonic sensor (repulsive forces), acting on the wheelchair. The resultant wheelchair's behavior is obtained by the sum of the attractive force and all the repulsive forces at a given position. Experimental results from drive tests in a cluttered office environment provided statistical evidence that the proposed algorithm is effective to reduce the number of collisions and still improve the user's safety perception. ©2010 IEEE.

Abstract
The Portuguese coastline, like many other worldwide coastlines, is often submitted to several types of extreme events resulting in erosion, thus, acquisition of high quality field measurements has become a common concern. The nearshore survey systems have been traditionally based on in situ measurements or in the use of satellite or aircraft mounted remote sensing systems. As an alternative, video-monitoring systems proved to be an economic and efficient way to collect useful and continuous data, and to document extreme events. In this context, is under development the project MoZCo (Advanced Methodologies and Techniques Development for Coastal Zone Monitoring), which intends to develop and implement monitoring techniques for the coastal zone based on a low cost video monitoring system. The pilot study area is Ofir beach (north of Portugal), a critical coastal area. In the beginning of this project (2010) a monitoring video station was developed, collecting snapshots and 10 minutes videos every hour. In order to process the data, several video image processing algorithms were implemented in Matlab (R), allowing achieve the main video-monitoring system products, such as, the shoreline detection. An algorithm based on image processing techniques was developed, using the HSV color space, the idea is to select a study and a sample area, containing pixels associated with dry and wet regions, over which a thresholding and some morphological operators are applied. After comparing the results with manual digitalization, promising results were achieved despite the method's simplicity, which is in continuous development in order to optimize the results.