FEEDBACK EQUALIZER FOR VEHICULAR CHANNEL

In this fast moving world, the number of fatal accidents is increasing day by day and this leads to the requirement of the availability of the traffic condition and road conditions related data to the users. Therefore, to support Vehicle-to-vehicle (V2V) communication in high speed mobility condition, it is required to have reliable and secure of communication. Here, the performance of multiple input and multiple output (MIMO) system as a combination of nonlinear decision feedback receiver (DFE) have been investigated in V2V channel. In this paper, through the simulation, the results are presented to show the effect of the channel correlation coefficient and Doppler shift (Fd) (because of the relative velocity of the vehicle) over the performance of the MIMO system. As a counter measure of those problems non-linear receivers have been formulated and analyzed

Multimedia Communication using DVB Technology Over Open Range

AbstractThe DVB technology is being used by millions of users across the globe in order to view television. This technology, processes the signal generated in one part of the globe by encoding it in multiple layers and sending them on a carrier wave which is being reflected by a satellite which is then received by the antennae at our home. The same widely proven technology may be re-used for ground based communication for its improvement which still has various limitations. Hence, in this paper,the development of a local open range communication system using DVB is being proposed in order to setup a communication link between two distant places

A survey on classification algorithms of brain images in Alzheimer’s disease based on feature extraction techniques

Abstract: Alzheimer’s disease (AD) is one of the most serious neurological disorders for elderly people. AD affected patient experiences severe memory loss. One of the main reasons for memory loss in AD patients is atrophy in the hippocampus, amygdala, etc. Due to the enormous growth of AD patients and the paucity of proper diagnostic tools, detection and classification of AD are considered as a challenging research area. Before a Cognitively normal (CN) person develops symptoms of AD, he may pass through an intermediate stage, commonly known as Mild Cognitive Impairment (MCI). MCI is having two stages, namely StableMCI (SMCI) and Progressive MCI (PMCI). In SMCI, a patient remains stable, whereas, in the case of PMCI, a person gradually develops few symptoms of AD. Several research works are in progress on the detection and classification of AD based on changes in the brain. In this paper, we have analyzed few existing state-of-art works for AD detection and classification, based on different feature extraction approaches. We have summarized the existing research articles with detailed observations. We have also compared the performance and research issues in each of the feature extraction mechanisms and observed that the AD classification using the wavelet transform-based feature extraction approaches might achieve convincing results