Papers by Keyword: Haar Wavelets

Abstract: This study investigates the depth and location of cracks in homogeneous Euler-Bernoulli beams with free vibrations. The problem is frequently encountered in industrial design and modeling, where an exact model requires the frequency output to be calibrated with a physical measure. The crack is simulated by a line spring model. The boundary value problem is solved using the Haar wavelets. The characteristic parameters are predicted with the aid of neural networks. The proposed method is compared to an alternative approach based on neural networks and several frequencies only. The significance of the complex approach of Haar wavelets and neural networks lies in its ability to make fast accurate model-independent predictions calculating only one natural frequency and training the network only once.

Abstract: Irresponsible driving is one of the leading causes of mishaps & deaths in the world today. The present work aims at developing a preventive solution to this problem in the form of a driving aid for drunk drivers or people who are not in a fit state to drive. The said system proposes to enhance certain existing frameworks using image processing. Haar-wavelet classifiers & Speeded Up Robust Features (SURF) algorithm are used to analyze images of the drivers' eyes & patterns of movement to judge if they are eligible to drive. These complement the existing sensors, like the odor sensor, and help the system reach a confident decision. We plan to take this system further by creating collaborative MANETs among vehicles for preventing potential harm to other drivers as well.

Abstract: One century ago (1910), the Hungarian mathematician Alfred Haar introduced the simplest wavelets in approximation theory, which are now known as the Haar wavelets. This type of wavelets can effectively be used to fit data in statistical applications. It is well known that for a general regression model, it is not easy to write estimations of its parameters in analytical forms. However, regression models generated from the Haar wavelets are easy to compute. In this article, we introduce how to use the Haar wavelets to formulate regression models and to fit data. In addition, we mention some variations of the Haar wavelets and their possible applications.