Satellite remote sensing is an important tool in the detection and short range forecasting (nowcasting) of fog events. Fog over land develops primarily during the late-night and pre-dawn hours, infrared remote sensing is indispensable in observing fog formation at night, while visible imagery helps to monitor the extent and density of fog after sunrise. Satellite remote sensing is widely used in the United States and Europe in detecting and nowcasting fog events. The temperature difference between two infrared bands (11 µm and 4 µm), available in geostationary satellites, forms the basis for fog detection and classification [1]. In this study an algorithm for now-casting, detecting and monitoring fog events in the United Arab Emirates was developed. The major approach is based on developing a neural-networks system in MATLAB, mainly using METEOSAT SEVIRI data and local meteorological data. The final developed tool is named UAE-FAD (Fog Automatic Detector) and will provide real-time information for further research and development as well as for public and civil services.

Today, various processes of seawater desalination are used to satisfy the increasing demand of fresh water. Desalination plants treat the seawater pumped from their intakes and discharge the generated waste back to the sea through their outfalls. The discharged waste, also called brine, has a high salinity level, high temperature and high concentration of other suspended matters which may cause negative environmental effects on the water quality surrounding the discharge point. In this paper, medium and high resolution satellite data collected by MODIS and DubaiSat-1 (DS-1), respectively, were used to derive average digital value and its spatial variability surrounding Jebel Ali plant. Satellite-derived sea surface temperature (SST) was derived and compared to field measured temperature collected between January and December 2008. In order to have a better understanding of the spectral and spatial characteristics of satellite data, the mean squared error (MSE) and peak signal to noise ratio (PSNR) were also calculated for different locations in the study area with determined window's size. Structural Similarity (SSIM) factor was used to compare two locations, one with pollution and the other without pollution.

This paper develops both theoretical and experimental models for the prediction of attenuation and reflectivity of satellite signals at Ku-band in the UAE. The theoretical model gives a detailed study on the effect of the attenuation and the reflectivity due to the increase of the frequency or the density of different hydrometers. The experimental model, which is based on actual measurements obtained from NASA, was used to extract the required precipitation radar (PR) products. By applying the image analysis techniques on the PR data for one whole year (2000), a graphical representation of attenuation at Ku-band in the UAE was obtained. Results of this paper are vital for the successful design of satellite TV broadcast systems in the UAE operating at Ku-band.

We propose an algorithm that automatically detects clear scene cut locations from an MPEG-1 video bit stream coded with a GOP structure of M=1, without B pictures. The algorithm detects scene cuts at P type pictures by monitoring the percentage of intramacroblocks per P picture. while scene cuts at I pictures are detected by matching the macroblock type of the two P pictures at the GOP boundaries. A "type matching parameter" (TMP) is developed to estimate the matching degree between the macroblock types of two P pictures. It is shown that the method is able to identify the location of scene cuts in P and I pictures with a high success rate.

We propose a classification method of MPEG coded video based on the image texture and motion levels of the scene content. The texture and motion are divided into three levels each, low, medium and high, resulting into nine scene categories. Since in MPEG-1 (and MPEG-2) encoder the DCT coefficients of the I-frame are affected by the image texture, while the motion affects the macroblocks coding mode of P- and B-frames, hence, an automatic scene classification can be obtained from the coded bit stream based on the scene's texture and motion levels. The method is useful for the content-based video analysis, such as video modeling and MPEG-7.

In this paper, we propose a video model to generate VBR MPEG video traffic based on the scene content description. Long sessions of nonhomogeneous video clips are decomposed into homogeneous video shots. The shots are then classified into different classes in terms of their texture and motion complexity. Each shot class was uniquely described with an autoregressive model. Transitions between the shots and their durations have been analyzed. Unlike many classical video source models, this model may be used to generate traffic of any type of video scenes ranging from a low complexity video conferencing to a highly active sport program. The performance of the model is evaluated by measuring the mean cell delay when the generated video traffic is fed to an ATM multiplex buffer.

We propose a video model to generate a VBR MPEG video traffic based on the scene description. Long sessions of non-homogeneous video clips are decomposed into homogeneous video shots. The shots are then classified into different classes in terms of their texture and motion complexity. Each shot class can be uniquely described and modelled as a homogeneous video. The model may be used to generate traffic of any type of video scenes ranging from a low complexity video conferencing to a highly active sport program.