CS or CE | 1-2 students | Research+Development project | Laboratory of Meteorological Computing project in collaboration with Hong Kong Observatory under the Cooperative Training Agreement
"Visibility" is defined as the greatest distance at which a black object of suitable dimensions, located near the ground, can be seen and recognized when observed against a scattering background of fog, sky, etc. This definition is based on human observation of atmospheric visibility conditions (see also reference 2 for details).
Instrumental observations of visibility are based on measurements of attenuation of light due to both scattering and absorption by particles in the air along the path of a light beam or by measuring the scatter coefficient of light within a volume of air. Both methods have their limitations as only a small volume of air is sampled in the determination of visibility and may produce large errors when the source of reduction in visibility is far away from the sampled air volume.
Nowadays, video cameras are widely used for remote monitoring and security surveillance purposes. Visibility estimation using digitized video images through identification of targets at known distance from the camera is a potential application. It has an intrinsic advantage over the instrumental light-absorption/scattered approaches in that the image acquisition processes between the camera lens system and the human eye are similar.
The use of images from video cameras for the automatic observation of visibility is non-trivial. The determination of visibility from images is affected by the size and shape of the reference targets, the contrast of the targets and their background and weather conditions (see the photos). The objective of this research topic is to continue the previous work on the same topic (see references 1 and 3) and develop an efficient automatic algorithm to compute visibility through the processing of images captured by video cameras.
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| 1(a) Photo with good contrast showing clearly distant hills that are over 20 km away | 1(b) Photo of the same view but with reduced visibility. A small island on the top right corner of the photo is just barely seen. |
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| 2(a) Photo affected significantly by the backlight caused by the morning sun | 2(b) Photo of the same place captured in the afternoon on the same day, with good contrast for the landmarks |
CS or CE | 1-2 students | More Research than development | Laboratory of Meteorological Computing project in collaboration with Hong Kong Observatory under the Cooperative Training Agreement
The project is to develop an algorithm to predict terrain-induced windshear over the Hong Kong International Airport (HKIA) based on meteorological measurements at the airport and its vicinity using artificial intelligence methods.
In the proposed project, Artificial Neural Network (ANN) and/or decision tree classifier techniques will be used to study the correlation between meteorological measurements over HKIA and its neighbouring areas. These include meteorological data collected over the past years such as surface wind, temperature, and windshear parameters retrieved from pilot reports of commercial aircraft or Winshear and Turbulence Warning System (WTWS) operating at HKIA. Advanced artificial intelligence techniques such as data mining, neural network etc. will be studied to derive the regression equations.
CS or CE | 1-2 students | Research+Development project | Laboratory of Meteorological Computing project in collaboration with Hong Kong Observatory under the Cooperative Training Agreement
The Hong Kong Observatory operates a lightning location network to detect lightning over the Pearl River Delta. Data collected so far reveal that the spatial distribution of lightning is not uniform throughout the region. A question arises: are some places more prone to lightning because of their special geographic features? The objective of this study is to find out whether geographic features such as topography, vegetation cover, water cover and urban structure affect the spatial distribution of lightning.
This project requires the study of a considerable amount of geographic and lightning data. Student will have the opportunity to learn and apply analytical skills such as spatial analysis and data mining.
CS or CE | 1-2 students | Research+Development project | Laboratory of Meteorological Computing project in collaboration with Hong Kong Observatory under the Cooperative Training Agreement
Measurements of gamma-emitting radionuclides in environmental samples are regularly performed using a gamma spectrometry system at the Radiation Laboratory at King's Park. The system comprises high purity germanium detectors, electronic processing modules and spectrometry analysis software.
The activities of the radionuclides are calculated using efficiency calibration curves which vary with photon energy, sample matrix, sample geometry, etc. Efficiency calibration is normally done using a standard solution with known concentrations of multi-radionuclides dissolved in water matrix. In practice, the measurements of radioactivity in environmental samples are influenced by the absorption of photons in the sample materials itself, which is often known as self-absorption. The environmental samples measured consist of many different sample types with different matrices, thus with different degrees of self-absorption.
The objective of the project is to build a software system for the estimation the self-absorption corrections for common matrices and geometries used in the laboratory.