Motorcycle accidents have been rapidly growing throughout the years in many countries. Due to various social and economic factors, this type of vehicle is becoming increasingly popular. The helmet is the main safety equipment of motorcyclists but many drivers do not use it. If a motorcyclist is without helmet an accident can be fatal. This paper presented an automatic method for vehicle detection, motorcycles classification on public roads and a system for automatic detection of motorcyclists without helmet. For processing, in first step, we detect vehicles that moving real-time by extracting back ground out from front ground using back subtraction then enhancing it using threshold and mathematical morphology method. In the second step, we classify between motorcycle and other vehicles. Area is applied for feature extraction and neural network is applied for classification. In the final step, Hough transform is applied for detecting a helmet. From the experimental results, the accuracy rates of the motorcycle classification and helmet detection were 98.22% and 77%, respectively.

No abstract

There exist Fast Fourier transform (FFT) algorithms, called dimensionless 1 FFTs, that work independent of dimension. These algorithms can be configured to compute different dimensional DFTs simply by relabeling the input data and by changing the values of the twiddle factors occurring in the butterfly operations. This observation allows us to design an FFT processor, which with minor reconfiguring, can compute one, two, and three dimensional DFTs. In this paper we design a family of FFT processors, parameterized by the number of points, the dimension, the number of processors, and the internal dataflow, and show how to map different dimensionless FFTs onto this hardware design. Different dimensionless FFTs have different dataflows and consequently lead to different performance characteristics. Using a performance model we search for the optimal algorithm for the family of processors we considered. The resulting algorithm and corresponding hardware design was implemented using FPGA.

Tonsillitis decease is a major cause of serious automatic extraction of semantic descriptions of medical deceases such as heart attack and pneumonia. This paper image content. They applied the mapping of image features proposes a computer-aided-diagnosis of Tonsillitis using Tonsil with a domain of semantic concepts of medical image size and color. In this method, we employ red color and tonsil contents. F. Arambula Cosio, J.A. Marquez Flores, M.A.

No abstract