Proceedings of the Technical Session of Institute of Physics, Sri Lanka 22 (2006) 1-15

Rainfall Forecasting: An Artificial Neural Network Approach
A.D. Kumarasiri and D.U.J. Sonnadara,
Department of Physics,University of Colombo, Colombo 3.

An innovative technique is utilized for rainfall forecasting using Artificial Neural Networks based on feed-forward back-propagation architecture. Focus is set upon making successful predictions from the available data, not on incorporating the physical aspects of the atmosphere or the actual process of rainfall occurrence. Both short term and long term forecasting was attempted for ground level data collected by the meteorological station in Colombo, Sri Lanka (Lat: 79.87 E, Long: 6.90 N, Altitude: 7.3 m).

Three Neural Network models were developed; a one-day-ahead model for predicting the rainfall occurrence of the next day, which was able to make predictions with a 74.25% accuracy, and two long term forecasting models for monthly and yearly rainfall depth predictions with 58.33% and 76.67% accuracies within a 5% uncertainty level. Each of these models was extended to make predictions several time steps into the future, where accuracies were found to be decreasing with the number of time steps. The success rates and rainfall trends within the monsoon seasons were also studied and presented.

Proceedings of the Technical Session of Institute of Physics, Sri Lanka 22 (2006) 17-24

Development of a Road Traffic Noise Prediction Model
R.T. Sooriyaarachchi and D.U.J. Sonnadara,
Department of Physics, University of Colombo, Colombo 3.

Environmental noise is an undesirable byproduct of urbanization. Although we do not notice, this unwanted or excessive sound makes a significant damage to the health of the people and has hazardous impacts on the environment. From the noise sources we interact daily, perhaps the most invasive and difficult to avoid noise source is the transportation noise. The major contributor to the transportation noise is highway traffic noise.

The objective of this work is to develop a road traffic noise prediction model for the roads of Sri Lanka. The developed model is capable of predicting the combined traffic noise generated from vehicles in highways. Traffic flow data used for constructing this model consisted of vehicle noise, vehicle class, vehicle speed and the distance from the traffic flow line was collected from several locations of the Western Province. Microsoft .Net® platform was used for the development of the simulator and the GUI. The predictions made by the model were found to be within ±11dBA accuracy with respect to the actual experimental observations.

Proceedings of the Technical Session of Institute of Physics, Sri Lanka 22 (2006) 25-36

Hardware Implementation of Random Number Generators
W. A. S. Wijesinghe¹, M. K. Jayananda ²,D. U. J. Sonnadara^2
1 Dept. of Electronics, Wayamba University of Sri Lanka, Kuliyapitiya, ² Dept. of Physics, University of Colombo, Colombo 3

Random numbers are used in a wide variety of applications. True random number generators are slow and expensive for many applications while pseudo random number generators (RNG) suffice for most applications. Although a majority of random number generators have been implemented in software level, increasing demand exists for hardware implementation due to the advent of faster and high density Field Programmable Gate Arrays (FPGA). FPGAs make it possible to implement complex systems, such as numerical calculations, genetic programs, simulation algorithms etc., at hardware level. This paper discusses in detail the hardware implementation of several RNGs and their characteristics. Somewhat complex Cellular Automata based RNGs show slightly improved performance compared to the simplest Linear Feedback Shift Register RNG.

Proceedings of the Technical Session of Institute of Physics, Sri Lanka 22 (2006) 37-45

Use of AMR Sensors for Lightning Magnetic Field Measurement
M. Fernando, S.R. Sharma, P. Hettiarachchi, and N. JayawanthaDepartmentof Physics, University of Colombo, Colombo 03.

Possibility of using compact Anisotropic Magneto-Resistive (AMR) sensors to measure the magnetic fields generated by Lightning flashes were tested in this study. Honeywell HMC1022 2-axis AMR sensor was selected for the measurements. Sensitivity of the measuring setup was increased using the LMH6624 op-amp. The measuring setup can measure magnetic fields as low as 300G in the range of DC to 4 MHz. Field measurements conducted in November 2005 in Colombo reveals that the setup can be used successfully for lightning magnetic field measurements. Due to the higher accuracy in angle and the amplitude suggests that the new setup can replace traditional bulky loop antennas in lightning magnetic field measurements as well as in lightning direction finders.

Proceedings of the Technical Session of Institute of Physics, Sri Lanka 22 (2006)

Presidential Address (2006): Student Understanding of Tunneling in Quantum Mechanics
S. R. D. RosaDepartmentof Physics, University of Colombo, Colombo 03.

We are studying student understanding of the phenomenon of quantum tunneling through a potential barrier, a standard topic in most introductory quantum physics courses. Series of interviews and tests revealed that many students believe energy is lost in the tunneling process. A survey was designed to investigate the prevalence of the energy-loss idea. This survey was administered to Physics special and engineering Physics special students of the department of Physics, University of Colombo.