Lane detection is an essential component of autonomous mobile robot applications. Any lane detection method has to deal with the varying conditions of the lane and surrounding that the robot would encounter while moving. Lane detection procedure can provide estimates for the position and orientation of the robot within the lane and also can provide a reference system for locating other obstacles in the path of the robot. In this paper we present a method for lane detection in video frames of a camera mounted on top of the mobile robot. Given video input from the camera, the gradient of the current lane in the near field of view are automatically detected. Randomized Hough Transform is used for extracting parametric curves from the images acquired. A priori knowledge of the lane position is assumed for better accuracy of lane detection.

Wavelet and Fourier transform are the common methods used in signal and image compression. Wavelet transform (WT) are very powerful compared to Fourier transform (FT) because its ability to describe any type of signals both in time and frequency domain simultaneously while for FT, it describes a signal from time domain to frequency domain. Because of that, the performance of FT is outperformed by the impressive ability of WT for most type of signals (stationary or non-stationary). In this paper, we will discuss the use of Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT) for signal compression. We do the numerical experiment by considering three types of signals and by applying FFT and DWT to decompose those signals. For DWT, various wavelet filters such as Haar (2 filters) and Daubechies (up to 10 filters) are used. All the numerical results were done by using Matlab programming.

Spatial interpolation is a method that can be used to estimate the unknown value (or parameter) at certain time or location. Besides of that, shape preserving interpolation especially positivity preserving interpolation play an important role for data visualization where the resulting interpolating surface must be positive everywhere. In this study, we use a triangulation based method consists of positivity preserving cubic Bí©zier triangular patches to estimate the unknown value of rainfall amount at spatial localization. This scheme is useful for spatial data interpolation compared to the mesh free methods which are required the optimization technique in order to ensure the positivity of basis functions.The data is triangulated by using Delaunay’s triangulation algorithm and from there the triangular surfaces are constructed piecewisely comprising three local schemes that are blended together via convex combination to form a scattered interpolating surface. Sufficient conditions on Bí©zier ordinates are derived in order to preserve the positivity of the surface. Validation of the propose schemes are conducted by using well-known test function used in previous researches. Then, the best the scheme is selected to visualize a rainfall data distribution based on secondary data collected at various meteorology stations in Malaysia obtain from Malaysian Meteorology Department. Finally, we estimate the unknown rainfall amount at certain locations that lies inside the convex hull formed by all given stations. The finding shows that positivity preserving cubic triangular patches is able to preserve the positivity of rainfall distribution surface and has a significant application in estimating the rainfall amount at unallocated meteorology stations in Malaysia.