Abstract
A method to produce rectified Advanced Very High Resolution Radiometer (AVHRR) datasets is proposed, using both the orbital model and identification of ground control points (GCPs), involving a single image transformation from the raw imagery. The ability of the orbital model to determine the geographical co-ordinates of pixels in a raw AVHRR image was tested, using a total of 1098 GCPs in 24 AVHRR images. Five of the 24 images were also rectified using the method proposed, using a newly identified set of GCPs. The differences between the geographical co-ordinates of the existing GCPs and those determined by the new method are calculated and discussed.

Abstract

Abstract
In this paper, we report experimental results on the frequency control of an optical oscillator based on a single-cavity DFB-MQW laser and a fiber loop reflector. Self-pulsation frequency can be controlled by changing the step amplitude of the laser bias current.

Abstract
In this paper we introduce a classification for spatio-temporal systems based on the properties of the represented objects. Building on this classification, we assert that complex objects can be derived from simpler ones, following an evolutionary approach which starts with the study of simple objects and ends by enriching them with new features. This paper focuses on the definition of a data model for the representation of moving points. The model is based on the decomposition of the trajectory of moving points into sections. The movement within each section of a trajectory is described by a variability function. Because for most systems is not possible to store the exact knowledge about the movement of an object, the answers to queries may be imprecise. We propose two additional approaches to deal with impression-the superset and the subset semantics-based on a maximum value for the variability function, and a smooth technique to integrate them in the model. Finally, we analyze certain functional aspects of the implementation of the data model in a Relational Database Management System (RDBMS) and outline directions for future research.

Abstract
In this article, two artificial neural networks based models for motion detection and motion analysis in image sequence, are proposed and analyzed. The first model is applicable to sequences acquired by a static camera, and segments the images into two areas: moving objects and background. The second model, applicable to image sequences obtained by a moving camera, uses a neural network to identify the `interesting points' in the images (e.g. edges, corners), and a block matching algorithm to calculate the movement in these points. Several tests with real images have been performed to evaluate the models. The results are similar to those obtained by other methods but the proposed techniques show some advantages which are discussed in the paper.

Abstract