In this article we present numerical solutions to multicellular natural convection in a vertical enclosure. The calculated streamlines faithfully represent what has been seen in the laboratory by smoke traces in air and particle traces in oils. The calculated isotherms for air correspond to reported interferometric patterns. Solutions exhibiting travelling waves for water were calculated near conditions where they should occur according to linear stability theory. Heat-transfer results for air are given and their dependence on the aspect ratio of the enclosure exhibited.

Abstract-Sky/cloud images captured by ground-based cameras (a.k.a. whole sky imagers) are increasingly used nowadays because of their applications in a number of fields, including climate modeling, weather prediction, renewable energy generation, and satellite communications. Due to the wide variety of cloud types and lighting conditions in such images, accurate and robust segmentation of clouds is challenging. In this paper, we present a supervised segmentation framework for ground-based sky/cloud images based on a systematic analysis of different color spaces and components, using partial least squares (PLS) regression. Unlike other state-of-the-art methods, our proposed approach is entirely learning-based and does not require any manuallydefined parameters. In addition, we release the Singapore Whole Sky IMaging SEGmentation Database (SWIMSEG), a large database of annotated sky/cloud images, to the research community.

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Site diversity is an effective rain attenuation mitigation technique, especially in the tropical region with high rainfall rate. The impact of different factors such as site separation distance, frequency, elevation angle, polarization angle, baseline orientation and wind direction is assessed. Results are compared to those reported in existing literature and also compared to the commonly used ITU-R site diversity prediction models. The effect of the wind direction on site diversity is also presented. It can be observed that diversity gain is highly dependent on the site separation distance, elevation angle and wind direction but independent of the frequency, baseline angle and polarization angle of the signal. This study is useful for the implementation of site diversity as a rain attenuation mitigation technique.

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Cloud imaging using ground-based whole sky imagers is essential for a fine-grained understanding of the effects of cloud formations, which can be useful in many applications. Some such imagers are available commercially, but their cost is relatively high, and their flexibility is limited. Therefore, we built a new daytime Whole Sky Imager (WSI) called Wide Angle High-Resolution Sky Imaging System. The strengths of our new design are its simplicity, low manufacturing cost and high resolution. Our imager captures the entire hemisphere in a single high-resolution picture via a digital camera using a fish-eye lens. The camera was modified to capture light across the visible as well as the near-infrared spectral ranges. This paper describes the design of the device as well as the geometric and radiometric calibration of the imaging system.

Abstract-In this paper, a large number of studies of the effect of the foliage on single or lines of trees on modern wireless communication systems are reviewed. The paper is focused on the experimental works mainly done for commercial applications such as cellular communication and high speed point-to-point fixed link at the microwave and millimeter wave frequencies. For this review study, the development of the foliage loss prediction methods and the factors influencing the tree-induced shadowing effect are highlighted. In view of current research work in this area, some possible future works are proposed to improve the performance of modern wireless communication systems with the effect of foliage.

Sky/cloud imaging using ground-based Whole Sky Imagers (WSI) is a cost-effective means to understanding cloud cover and weather patterns. The accurate segmentation of clouds in these images is a challenging task, as clouds do not possess any clear structure. Several algorithms using different color models have been proposed in the literature. This paper presents a systematic approach for the selection of color spaces and components for optimal segmentation of sky/cloud images. Using mainly principal component analysis (PCA) and fuzzy clustering for evaluation, we identify the most suitable color components for this task.