In robotics, mechanized and computer simulation for accurate and fast crash detection between general geometric models is a fundamental problem. The explanation of this problem will gravely improve driver safety and traffic efficiency, vehicular ad hoc networks (VANETs) have been employed in many scenarios to provide road safety and for convenient travel of the people. They offer self-organizing decentralized environments to disseminate traffic data, vehicle information and hazardous events. In order to avoid accidents during roadway travels, which are a major burden to the society, the data, such as traffic data, vehicle data and the road condition, play a critical role. VANET is employed for disseminating the data. Still the scalability issues occur when the communication happens under high-traffic regime where the vehicle density is high. The data redundancy and packet collisions may be high which cause broadcast storm problems. Here the traffic regime in the current state is obtained from the speed of the vehicle. Thus the data reduction is obtained. In order to suppress the redundant broadcast D-SAC data, dissemination protocol is presented in this paper. Here the data are classified according to its criticality and the probability is determined. The performance of the D-SAC protocol is verified through conventional methods with simulation.
The analysis of WDM (Wavelength-Division Multiplexing) optical network is essential to have the routed wavelength blocking probability with the conversion of wavelength using techniques. In this paper, an enhanced analytical model is proposed to evaluate the blocking performances in topology network and to improve the performances of reduction of blocking probability. The variation of probability is based on the wavelength and load used in the network. The conversion is carried out with the support of optical backbone of the inherent flexibility of the network using the proposed IMCA in Sparse-Partial Wavelength Conversion (SPWC) architecture. It reduces the number of converters significantly with efficient process and provides placement scheme of wavelength converters in the network. The proposed model utilizes the network with the assignment and routing of wavelength using dynamic process of assignment algorithm. The proposed model provides dynamic and static routing process with the range limit to have a minimum conversion for the same probabilities of blocking. The proposed system analysis and the simulation results show the better performances in faster coverage, minimum number of conversions, blocking probability improvement for high load.
In the present work,an image extraction technique has been designed to fix the center spot of the tropical cyclone. The cyclone prediction is processed within the CBIR system and hence it established a well-effective output. A raw cyclone image is being processed under the image processing techniques such as Adaptive Gaussian Notch Filter (Pre-processing), Adaptive Thresholding (segmentation) and Feature-extraction. The extracted features are collectively used as an input to the CNN-HMO classifier which produces a relevant classified output. Relevance feedback is a key factor which passes these corresponding outputs to the user. User selects the required cyclone image after then, the image extraction process of PSO based density matrix takes place. The extractor has variance, gradient and density matrix (DM) to predit the center spot. So that the brightness temperature is computed for each pixel coordinates in both zonal and meridional axial direction. After then DM point converges over every pixel in an image and a PSO is followed towards the pixel orientation of each convergence line to determine the best optimal solution (i.e.,) the center of the Tropical Cyclone. With this optimizing criterion, the center of the Tropical Cyclone is generated.
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